{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/denoising/papers/ran/3","list_of":"/task/denoising","task":"Denoising","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":3,"pages_in_order":9,"rows_per_page":100,"rows":[201,300],"of":832,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/denoising/papers/ran/1","prev":"/task/denoising/papers/ran/2","next":"/task/denoising/papers/ran/4","papers":[{"url":"/paper/proxy-denoising-for-source-free-domain","slug":"proxy-denoising-for-source-free-domain","title":"Proxy Denoising for Source-Free Domain Adaptation","date":"2024-06-03","arxiv_id":"2406.01658","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/proxy-denoising-for-source-free-domain#ran","syntology_url":"https://syntology.ai/paper/2406.01658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01658"}},"official":{"repos":["tntek/source-free-domain-adaptation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-cache-accelerating-diffusion","slug":"learning-to-cache-accelerating-diffusion","title":"Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching","date":"2024-06-03","arxiv_id":"2406.01733","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-cache-accelerating-diffusion#ran","syntology_url":"https://syntology.ai/paper/2406.01733","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01733"}},"official":{"repos":["horseee/learning-to-cache"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/deft-efficient-finetuning-of-conditional","slug":"deft-efficient-finetuning-of-conditional","title":"DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform","date":"2024-06-03","arxiv_id":"2406.01781","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deft-efficient-finetuning-of-conditional#ran","syntology_url":"https://syntology.ai/paper/2406.01781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01781"}},"official":{"repos":["alexdenker/deft"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mixed-diffusion-for-3d-indoor-scene-synthesis","slug":"mixed-diffusion-for-3d-indoor-scene-synthesis","title":"Mixed Diffusion for 3D Indoor Scene Synthesis","date":"2024-05-31","arxiv_id":"2405.21066","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mixed-diffusion-for-3d-indoor-scene-synthesis#ran","syntology_url":"https://syntology.ai/paper/2405.21066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.21066"}},"official":{"repos":["mit-spark/midiffusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-policies-creating-a-trust-region","slug":"diffusion-policies-creating-a-trust-region","title":"Diffusion Policies creating a Trust Region for Offline Reinforcement Learning","date":"2024-05-30","arxiv_id":"2405.19690","repositories_listed":1,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":11,"n_instrument":6,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":20,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/diffusion-policies-creating-a-trust-region#ran","syntology_url":"https://syntology.ai/paper/2405.19690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19690"}},"official":{"repos":["tianyucodings/diffusion_trusted_q_learning"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/all-in-one-medical-image-restoration-via-task","slug":"all-in-one-medical-image-restoration-via-task","title":"All-In-One Medical Image Restoration via Task-Adaptive Routing","date":"2024-05-30","arxiv_id":"2405.19769","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/all-in-one-medical-image-restoration-via-task#ran","syntology_url":"https://syntology.ai/paper/2405.19769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19769"}},"official":{"repos":["yaziwel/all-in-one-medical-image-restoration-via-task-adaptive-routing"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rnaflow-rna-structure-sequence-design-via","slug":"rnaflow-rna-structure-sequence-design-via","title":"RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching","date":"2024-05-29","arxiv_id":"2405.18768","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rnaflow-rna-structure-sequence-design-via#ran","syntology_url":"https://syntology.ai/paper/2405.18768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18768"}},"official":{"repos":["divnori/rnaflow"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ov-dquo-open-vocabulary-detr-with-denoising","slug":"ov-dquo-open-vocabulary-detr-with-denoising","title":"OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision","date":"2024-05-28","arxiv_id":"2405.17913","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ov-dquo-open-vocabulary-detr-with-denoising#ran","syntology_url":"https://syntology.ai/paper/2405.17913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17913"}},"official":{"repos":["xiaomoguhz/ov-dquo"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/promptfix-you-prompt-and-we-fix-the-photo","slug":"promptfix-you-prompt-and-we-fix-the-photo","title":"PromptFix: You Prompt and We Fix the Photo","date":"2024-05-27","arxiv_id":"2405.16785","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":2,"n_no_contract":7,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 2 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/promptfix-you-prompt-and-we-fix-the-photo#ran","syntology_url":"https://syntology.ai/paper/2405.16785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16785"}},"official":{"repos":["yeates/promptfix"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/glauber-generative-model-discrete-diffusion","slug":"glauber-generative-model-discrete-diffusion","title":"Glauber Generative Model: Discrete Diffusion Models via Binary Classification","date":"2024-05-27","arxiv_id":"2405.17035","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glauber-generative-model-discrete-diffusion#ran","syntology_url":"https://syntology.ai/paper/2405.17035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17035"}},"official":null}},{"url":"/paper/ensembling-diffusion-models-via-adaptive","slug":"ensembling-diffusion-models-via-adaptive","title":"Ensembling Diffusion Models via Adaptive Feature Aggregation","date":"2024-05-27","arxiv_id":"2405.17082","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ensembling-diffusion-models-via-adaptive#ran","syntology_url":"https://syntology.ai/paper/2405.17082","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17082"}},"official":{"repos":["tenvence/afa"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-discretize-denoising-diffusion","slug":"learning-to-discretize-denoising-diffusion","title":"Learning to Discretize Denoising Diffusion ODEs","date":"2024-05-24","arxiv_id":"2405.15506","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":15,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-discretize-denoising-diffusion#ran","syntology_url":"https://syntology.ai/paper/2405.15506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15506"}},"official":{"repos":["vinhsuhi/ld3"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-bridge-implicit-models","slug":"diffusion-bridge-implicit-models","title":"Diffusion Bridge Implicit Models","date":"2024-05-24","arxiv_id":"2405.15885","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/diffusion-bridge-implicit-models#ran","syntology_url":"https://syntology.ai/paper/2405.15885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15885"}},"official":{"repos":["thu-ml/diffusionbridge"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-schrodinger-bridge-matching","slug":"adversarial-schrodinger-bridge-matching","title":"Adversarial Schrödinger Bridge Matching","date":"2024-05-23","arxiv_id":"2405.14449","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarial-schrodinger-bridge-matching#ran","syntology_url":"https://syntology.ai/paper/2405.14449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14449"}},"official":{"repos":["daniil-selikhanovych/asbm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/conditioning-diffusion-models-by-explicit","slug":"conditioning-diffusion-models-by-explicit","title":"Conditioning diffusion models by explicit forward-backward bridging","date":"2024-05-22","arxiv_id":"2405.13794","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/conditioning-diffusion-models-by-explicit#ran","syntology_url":"https://syntology.ai/paper/2405.13794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13794"}},"official":{"repos":["zgbkdlm/fbs"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unmarker-a-universal-attack-on-defensive","slug":"unmarker-a-universal-attack-on-defensive","title":"UnMarker: A Universal Attack on Defensive Image Watermarking","date":"2024-05-14","arxiv_id":"2405.08363","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":19,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/unmarker-a-universal-attack-on-defensive#ran","syntology_url":"https://syntology.ai/paper/2405.08363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.08363"}},"official":{"repos":["andrekassis/ai-watermark"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/structure-based-drug-design-by-denoising","slug":"structure-based-drug-design-by-denoising","title":"Structure-based drug design by denoising voxel grids","date":"2024-05-07","arxiv_id":"2405.03961","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/structure-based-drug-design-by-denoising#ran","syntology_url":"https://syntology.ai/paper/2405.03961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03961"}},"official":{"repos":["genentech/voxbind"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adsorbdiff-adsorbate-placement-via","slug":"adsorbdiff-adsorbate-placement-via","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","date":"2024-05-07","arxiv_id":"2405.03962","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adsorbdiff-adsorbate-placement-via#ran","syntology_url":"https://syntology.ai/paper/2405.03962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.03962"}},"official":{"repos":["AdeeshKolluru/AdsorbDiff"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diff-ip2d-diffusion-based-hand-object","slug":"diff-ip2d-diffusion-based-hand-object","title":"Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos","date":"2024-05-07","arxiv_id":"2405.04370","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/diff-ip2d-diffusion-based-hand-object#ran","syntology_url":"https://syntology.ai/paper/2405.04370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04370"}},"official":{"repos":["irmvlab/diff-ip2d"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/locinv-localization-aware-inversion-for-text","slug":"locinv-localization-aware-inversion-for-text","title":"LocInv: Localization-aware Inversion for Text-Guided Image Editing","date":"2024-05-02","arxiv_id":"2405.01496","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/locinv-localization-aware-inversion-for-text#ran","syntology_url":"https://syntology.ai/paper/2405.01496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01496"}},"official":{"repos":["wangkai930418/DPL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-filtering-based-on-diffusion","slug":"collaborative-filtering-based-on-diffusion","title":"Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity","date":"2024-04-22","arxiv_id":"2404.14240","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/collaborative-filtering-based-on-diffusion#ran","syntology_url":"https://syntology.ai/paper/2404.14240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14240"}},"official":{"repos":["jackfrost168/cf_diff"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/global-counterfactual-directions","slug":"global-counterfactual-directions","title":"Global Counterfactual Directions","date":"2024-04-18","arxiv_id":"2404.12488","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/global-counterfactual-directions#ran","syntology_url":"https://syntology.ai/paper/2404.12488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.12488"}},"official":{"repos":["sobieskibj/gcd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/molecular-relaxation-by-reverse-diffusion","slug":"molecular-relaxation-by-reverse-diffusion","title":"Molecular relaxation by reverse diffusion with time step prediction","date":"2024-04-16","arxiv_id":"2404.10935","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/molecular-relaxation-by-reverse-diffusion#ran","syntology_url":"https://syntology.ai/paper/2404.10935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10935"}},"official":{"repos":["khaledkah/morered"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/digging-into-contrastive-learning-for-robust","slug":"digging-into-contrastive-learning-for-robust","title":"Digging into contrastive learning for robust depth estimation with diffusion models","date":"2024-04-15","arxiv_id":"2404.09831","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/digging-into-contrastive-learning-for-robust#ran","syntology_url":"https://syntology.ai/paper/2404.09831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09831"}},"official":{"repos":["wangjiyuan9/d4rd"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/tbsn-transformer-based-blind-spot-network-for","slug":"tbsn-transformer-based-blind-spot-network-for","title":"Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising","date":"2024-04-11","arxiv_id":"2404.07846","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tbsn-transformer-based-blind-spot-network-for#ran","syntology_url":"https://syntology.ai/paper/2404.07846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07846"}},"official":{"repos":["nagejacob/tbsn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/taming-stable-diffusion-for-text-to-360deg","slug":"taming-stable-diffusion-for-text-to-360deg","title":"Taming Stable Diffusion for Text to 360° Panorama Image Generation","date":"2024-04-11","arxiv_id":"2404.07949","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/taming-stable-diffusion-for-text-to-360deg#ran","syntology_url":"https://syntology.ai/paper/2404.07949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07949"}},"official":{"repos":["chengzhag/panfusion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-modeling-duo-towards-a-universal-audio","slug":"masked-modeling-duo-towards-a-universal-audio","title":"Masked Modeling Duo: Towards a Universal Audio Pre-training Framework","date":"2024-04-09","arxiv_id":"2404.06095","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-modeling-duo-towards-a-universal-audio#ran","syntology_url":"https://syntology.ai/paper/2404.06095","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06095"}},"official":{"repos":["nttcslab/m2d","nttcslab/eval-audio-repr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-the-spatial-inconsistency-in","slug":"rethinking-the-spatial-inconsistency-in","title":"Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance","date":"2024-04-08","arxiv_id":"2404.05384","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/rethinking-the-spatial-inconsistency-in#ran","syntology_url":"https://syntology.ai/paper/2404.05384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05384"}},"official":{"repos":["smilesdzgk/s-cfg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/gaussian-shading-provable-performance","slug":"gaussian-shading-provable-performance","title":"Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models","date":"2024-04-07","arxiv_id":"2404.04956","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":10,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gaussian-shading-provable-performance#ran","syntology_url":"https://syntology.ai/paper/2404.04956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04956"}},"official":{"repos":["bsmhmmlf/Gaussian-Shading"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-scale-transformer-for-large-scale-single","slug":"dual-scale-transformer-for-large-scale-single","title":"Dual-Scale Transformer for Large-Scale Single-Pixel Imaging","date":"2024-04-07","arxiv_id":"2404.05001","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":3,"n_ran_checked":5,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/dual-scale-transformer-for-large-scale-single#ran","syntology_url":"https://syntology.ai/paper/2404.05001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05001"}},"official":{"repos":["gang-qu/hatnet-spi"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/wcdt-world-centric-diffusion-transformer-for","slug":"wcdt-world-centric-diffusion-transformer-for","title":"WcDT: World-centric Diffusion Transformer for Traffic Scene Generation","date":"2024-04-02","arxiv_id":"2404.02082","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wcdt-world-centric-diffusion-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2404.02082","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02082"}},"official":{"repos":["yangchen1997/wcdt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-2-dynamic-3d-content-generation-via","slug":"diffusion-2-dynamic-3d-content-generation-via","title":"Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models","date":"2024-04-02","arxiv_id":"2404.02148","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":7,"n_pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 3 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-2-dynamic-3d-content-generation-via#ran","syntology_url":"https://syntology.ai/paper/2404.02148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02148"}},"official":{"repos":["fudan-zvg/diffusion-square"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/dynamic-pre-training-towards-efficient-and","slug":"dynamic-pre-training-towards-efficient-and","title":"Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration","date":"2024-04-02","arxiv_id":"2404.02154","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dynamic-pre-training-towards-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2404.02154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02154"}},"official":{"repos":["akshaydudhane16/dynet"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/drag-your-noise-interactive-point-based","slug":"drag-your-noise-interactive-point-based","title":"Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation","date":"2024-04-01","arxiv_id":"2404.01050","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":17,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/drag-your-noise-interactive-point-based#ran","syntology_url":"https://syntology.ai/paper/2404.01050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01050"}},"official":{"repos":["haofengl/dragnoise"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/denoising-monte-carlo-renders-with-diffusion","slug":"denoising-monte-carlo-renders-with-diffusion","title":"Denoising Monte Carlo Renders with Diffusion Models","date":"2024-03-30","arxiv_id":"2404.00491","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":12,"n_instrument":4,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":9,"n_pointer_only":18,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 3 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/denoising-monte-carlo-renders-with-diffusion#ran","syntology_url":"https://syntology.ai/paper/2404.00491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00491"}},"official":{"repos":["vibe007/Denoising_MC_Renders_Diffusion"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/structure-matters-tackling-the-semantic","slug":"structure-matters-tackling-the-semantic","title":"Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting","date":"2024-03-29","arxiv_id":"2403.19898","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":6,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/structure-matters-tackling-the-semantic#ran","syntology_url":"https://syntology.ai/paper/2403.19898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19898"}},"official":{"repos":["htyjers/strdiffusion"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/binarized-low-light-raw-video-enhancement","slug":"binarized-low-light-raw-video-enhancement","title":"Binarized Low-light Raw Video Enhancement","date":"2024-03-29","arxiv_id":"2403.19944","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/binarized-low-light-raw-video-enhancement#ran","syntology_url":"https://syntology.ai/paper/2403.19944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19944"}},"official":{"repos":["ying-fu/brve"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/qncd-quantization-noise-correction-for","slug":"qncd-quantization-noise-correction-for","title":"QNCD: Quantization Noise Correction for Diffusion Models","date":"2024-03-28","arxiv_id":"2403.19140","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":3,"n_instrument":8,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 8 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/qncd-quantization-noise-correction-for#ran","syntology_url":"https://syntology.ai/paper/2403.19140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19140"}},"official":{"repos":["huanpengchu/qncd"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ship-in-sight-diffusion-models-for-ship-image","slug":"ship-in-sight-diffusion-models-for-ship-image","title":"Ship in Sight: Diffusion Models for Ship-Image Super Resolution","date":"2024-03-27","arxiv_id":"2403.18370","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ship-in-sight-diffusion-models-for-ship-image#ran","syntology_url":"https://syntology.ai/paper/2403.18370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18370"}},"official":{"repos":["luigisigillo/shipinsight"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/singulartrajectory-universal-trajectory","slug":"singulartrajectory-universal-trajectory","title":"SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model","date":"2024-03-27","arxiv_id":"2403.18452","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/singulartrajectory-universal-trajectory#ran","syntology_url":"https://syntology.ai/paper/2403.18452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18452"}},"official":{"repos":["inhwanbae/singulartrajectory"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-rectifying-diffusion-sampling-with","slug":"self-rectifying-diffusion-sampling-with","title":"Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance","date":"2024-03-26","arxiv_id":"2403.17377","repositories_listed":3,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":12,"n_instrument":3,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 3 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-rectifying-diffusion-sampling-with#ran","syntology_url":"https://syntology.ai/paper/2403.17377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17377"}},"official":{"repos":["KU-CVLAB/Perturbed-Attention-Guidance"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/building-bridges-across-spatial-and-temporal","slug":"building-bridges-across-spatial-and-temporal","title":"Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model","date":"2024-03-26","arxiv_id":"2403.17460","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/building-bridges-across-spatial-and-temporal#ran","syntology_url":"https://syntology.ai/paper/2403.17460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17460"}},"official":{"repos":["dongrunmin/refdiff"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/noise2noise-denoising-of-crism-hyperspectral","slug":"noise2noise-denoising-of-crism-hyperspectral","title":"Noise2Noise Denoising of CRISM Hyperspectral Data","date":"2024-03-26","arxiv_id":"2403.17757","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/noise2noise-denoising-of-crism-hyperspectral#ran","syntology_url":"https://syntology.ai/paper/2403.17757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17757"}},"official":{"repos":["rob-platt/n2n4m"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/make-your-anchor-a-diffusion-based-2d-avatar","slug":"make-your-anchor-a-diffusion-based-2d-avatar","title":"Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework","date":"2024-03-25","arxiv_id":"2403.16510","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/make-your-anchor-a-diffusion-based-2d-avatar#ran","syntology_url":"https://syntology.ai/paper/2403.16510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.16510"}},"official":{"repos":["ictmcg/make-your-anchor"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/be-yourself-bounded-attention-for-multi","slug":"be-yourself-bounded-attention-for-multi","title":"Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation","date":"2024-03-25","arxiv_id":"2403.16990","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/be-yourself-bounded-attention-for-multi#ran","syntology_url":"https://syntology.ai/paper/2403.16990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.16990"}},"official":null}},{"url":"/paper/provably-robust-score-based-diffusion","slug":"provably-robust-score-based-diffusion","title":"Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction","date":"2024-03-25","arxiv_id":"2403.17042","repositories_listed":1,"syntology":{"n":28,"n_ran":18,"n_constructed":0,"n_ran_checked":12,"n_instrument":6,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":28,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 6 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/provably-robust-score-based-diffusion#ran","syntology_url":"https://syntology.ai/paper/2403.17042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17042"}},"official":{"repos":["x1xu/diffusion-plug-and-play"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/synctweedies-a-general-generative-framework","slug":"synctweedies-a-general-generative-framework","title":"SyncTweedies: A General Generative Framework Based on Synchronized Diffusions","date":"2024-03-21","arxiv_id":"2403.14370","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/synctweedies-a-general-generative-framework#ran","syntology_url":"https://syntology.ai/paper/2403.14370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14370"}},"official":null}},{"url":"/paper/mulde-multiscale-log-density-estimation-via","slug":"mulde-multiscale-log-density-estimation-via","title":"MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection","date":"2024-03-21","arxiv_id":"2403.14497","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mulde-multiscale-log-density-estimation-via#ran","syntology_url":"https://syntology.ai/paper/2403.14497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14497"}},"official":{"repos":["jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/renoise-real-image-inversion-through","slug":"renoise-real-image-inversion-through","title":"ReNoise: Real Image Inversion Through Iterative Noising","date":"2024-03-21","arxiv_id":"2403.14602","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/renoise-real-image-inversion-through#ran","syntology_url":"https://syntology.ai/paper/2403.14602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14602"}},"official":null}},{"url":"/paper/adair-adaptive-all-in-one-image-restoration","slug":"adair-adaptive-all-in-one-image-restoration","title":"AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation","date":"2024-03-21","arxiv_id":"2403.14614","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/adair-adaptive-all-in-one-image-restoration#ran","syntology_url":"https://syntology.ai/paper/2403.14614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14614"}},"official":{"repos":["c-yn/adair"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-diffusion-models-to-real-world-3d","slug":"scaling-diffusion-models-to-real-world-3d","title":"Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion","date":"2024-03-20","arxiv_id":"2403.13470","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scaling-diffusion-models-to-real-world-3d#ran","syntology_url":"https://syntology.ai/paper/2403.13470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13470"}},"official":{"repos":["prbonn/lidiff"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generalized-consistency-trajectory-models-for","slug":"generalized-consistency-trajectory-models-for","title":"Generalized Consistency Trajectory Models for Image Manipulation","date":"2024-03-19","arxiv_id":"2403.12510","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/generalized-consistency-trajectory-models-for#ran","syntology_url":"https://syntology.ai/paper/2403.12510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12510"}},"official":{"repos":["1202kbs/gctm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/tuning-free-image-customization-with-image","slug":"tuning-free-image-customization-with-image","title":"Tuning-Free Image Customization with Image and Text Guidance","date":"2024-03-19","arxiv_id":"2403.12658","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":6,"n_pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 3 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tuning-free-image-customization-with-image#ran","syntology_url":"https://syntology.ai/paper/2403.12658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12658"}},"official":{"repos":["zrealli/TIGIC"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/you-only-sample-once-taming-one-step-text-to","slug":"you-only-sample-once-taming-one-step-text-to","title":"You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs","date":"2024-03-19","arxiv_id":"2403.12931","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/you-only-sample-once-taming-one-step-text-to#ran","syntology_url":"https://syntology.ai/paper/2403.12931","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12931"}},"official":{"repos":["luo-yihong/yoso"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/divide-and-conquer-posterior-sampling-for","slug":"divide-and-conquer-posterior-sampling-for","title":"Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors","date":"2024-03-18","arxiv_id":"2403.11407","repositories_listed":1,"syntology":{"n":17,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":17,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/divide-and-conquer-posterior-sampling-for#ran","syntology_url":"https://syntology.ai/paper/2403.11407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11407"}},"official":{"repos":["badr-moufad/dcps"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/vmambair-visual-state-space-model-for-image","slug":"vmambair-visual-state-space-model-for-image","title":"VmambaIR: Visual State Space Model for Image Restoration","date":"2024-03-18","arxiv_id":"2403.11423","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vmambair-visual-state-space-model-for-image#ran","syntology_url":"https://syntology.ai/paper/2403.11423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11423"}},"official":{"repos":["alphacatplus/vmambair"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/one-step-image-translation-with-text-to-image","slug":"one-step-image-translation-with-text-to-image","title":"One-Step Image Translation with Text-to-Image Models","date":"2024-03-18","arxiv_id":"2403.12036","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/one-step-image-translation-with-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2403.12036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12036"}},"official":{"repos":["gaparmar/img2img-turbo"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/omg-occlusion-friendly-personalized-multi","slug":"omg-occlusion-friendly-personalized-multi","title":"OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models","date":"2024-03-16","arxiv_id":"2403.10983","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":13,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/omg-occlusion-friendly-personalized-multi#ran","syntology_url":"https://syntology.ai/paper/2403.10983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10983"}},"official":{"repos":["kongzhecn/omg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/hybrid-convolutional-and-attention-network","slug":"hybrid-convolutional-and-attention-network","title":"Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising","date":"2024-03-15","arxiv_id":"2403.10067","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hybrid-convolutional-and-attention-network#ran","syntology_url":"https://syntology.ai/paper/2403.10067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10067"}},"official":{"repos":["summitgao/hcanet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/switch-diffusion-transformer-synergizing","slug":"switch-diffusion-transformer-synergizing","title":"Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts","date":"2024-03-14","arxiv_id":"2403.09176","repositories_listed":2,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":9,"n_instrument":6,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/switch-diffusion-transformer-synergizing#ran","syntology_url":"https://syntology.ai/paper/2403.09176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09176"}},"official":{"repos":["byeongjun-park/Switch-DiT"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/score-guided-diffusion-for-3d-human-recovery","slug":"score-guided-diffusion-for-3d-human-recovery","title":"Score-Guided Diffusion for 3D Human Recovery","date":"2024-03-14","arxiv_id":"2403.09623","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/score-guided-diffusion-for-3d-human-recovery#ran","syntology_url":"https://syntology.ai/paper/2403.09623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09623"}},"official":{"repos":["statho/scorehmr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/noisediffusion-correcting-noise-for-image","slug":"noisediffusion-correcting-noise-for-image","title":"NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation","date":"2024-03-13","arxiv_id":"2403.08840","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":1,"n_ran_checked":4,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/noisediffusion-correcting-noise-for-image#ran","syntology_url":"https://syntology.ai/paper/2403.08840","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08840"}},"official":{"repos":["tmlr-group/noisediffusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-text-frozen-large-language-models-in","slug":"beyond-text-frozen-large-language-models-in","title":"Beyond Text: Frozen Large Language Models in Visual Signal Comprehension","date":"2024-03-12","arxiv_id":"2403.07874","repositories_listed":1,"syntology":{"n":26,"n_ran":17,"n_constructed":0,"n_ran_checked":6,"n_instrument":11,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":26,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 11 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/beyond-text-frozen-large-language-models-in#ran","syntology_url":"https://syntology.ai/paper/2403.07874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07874"}},"official":{"repos":["zh460045050/v2l-tokenizer"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupled-data-consistency-with-diffusion","slug":"decoupled-data-consistency-with-diffusion","title":"Decoupled Data Consistency with Diffusion Purification for Image Restoration","date":"2024-03-10","arxiv_id":"2403.06054","repositories_listed":1,"syntology":{"n":19,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":19,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/decoupled-data-consistency-with-diffusion#ran","syntology_url":"https://syntology.ai/paper/2403.06054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06054"}},"official":{"repos":["morefre/decoupled-data-consistency-with-diffusion-purification-for-image-restoration"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-image-to-image-schrodinger-bridge","slug":"implicit-image-to-image-schrodinger-bridge","title":"Implicit Image-to-Image Schrodinger Bridge for Image Restoration","date":"2024-03-10","arxiv_id":"2403.06069","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/implicit-image-to-image-schrodinger-bridge#ran","syntology_url":"https://syntology.ai/paper/2403.06069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06069"}},"official":{"repos":["wangya22/I3SB"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/ella-equip-diffusion-models-with-llm-for","slug":"ella-equip-diffusion-models-with-llm-for","title":"ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment","date":"2024-03-08","arxiv_id":"2403.05135","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ella-equip-diffusion-models-with-llm-for#ran","syntology_url":"https://syntology.ai/paper/2403.05135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05135"}},"official":null}},{"url":"/paper/diffsf-diffusion-models-for-scene-flow","slug":"diffsf-diffusion-models-for-scene-flow","title":"DiffSF: Diffusion Models for Scene Flow Estimation","date":"2024-03-08","arxiv_id":"2403.05327","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffsf-diffusion-models-for-scene-flow#ran","syntology_url":"https://syntology.ai/paper/2403.05327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05327"}},"official":{"repos":["zhangyushan3/diffsf"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/noisecollage-a-layout-aware-text-to-image","slug":"noisecollage-a-layout-aware-text-to-image","title":"NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging","date":"2024-03-06","arxiv_id":"2403.03485","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/noisecollage-a-layout-aware-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2403.03485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03485"}},"official":{"repos":["univ-esuty/noisecollage"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dpot-auto-regressive-denoising-operator","slug":"dpot-auto-regressive-denoising-operator","title":"DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training","date":"2024-03-06","arxiv_id":"2403.03542","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dpot-auto-regressive-denoising-operator#ran","syntology_url":"https://syntology.ai/paper/2403.03542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03542"}},"official":{"repos":["HaoZhongkai/DPOT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/diffusion-ts-interpretable-diffusion-for","slug":"diffusion-ts-interpretable-diffusion-for","title":"Diffusion-TS: Interpretable Diffusion for General Time Series Generation","date":"2024-03-04","arxiv_id":"2403.01742","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-ts-interpretable-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2403.01742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01742"}},"official":{"repos":["y-debug-sys/diffusion-ts"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ootdiffusion-outfitting-fusion-based-latent","slug":"ootdiffusion-outfitting-fusion-based-latent","title":"OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on","date":"2024-03-04","arxiv_id":"2403.01779","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ootdiffusion-outfitting-fusion-based-latent#ran","syntology_url":"https://syntology.ai/paper/2403.01779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01779"}},"official":{"repos":["levihsu/ootdiffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/viewdiff-3d-consistent-image-generation-with","slug":"viewdiff-3d-consistent-image-generation-with","title":"ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models","date":"2024-03-04","arxiv_id":"2403.01807","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/viewdiff-3d-consistent-image-generation-with#ran","syntology_url":"https://syntology.ai/paper/2403.01807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01807"}},"official":{"repos":["facebookresearch/viewdiff"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/viewfusion-towards-multi-view-consistency-via","slug":"viewfusion-towards-multi-view-consistency-via","title":"ViewFusion: Towards Multi-View Consistency via Interpolated Denoising","date":"2024-02-29","arxiv_id":"2402.18842","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/viewfusion-towards-multi-view-consistency-via#ran","syntology_url":"https://syntology.ai/paper/2402.18842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18842"}},"official":{"repos":["Wi-sc/ViewFusion"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/generating-reconstructing-and-representing","slug":"generating-reconstructing-and-representing","title":"Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding","date":"2024-02-29","arxiv_id":"2402.19009","repositories_listed":1,"syntology":{"n":24,"n_ran":22,"n_constructed":13,"n_ran_checked":18,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":19,"phrase":"22 ran (of which 13 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generating-reconstructing-and-representing#ran","syntology_url":"https://syntology.ai/paper/2402.19009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19009"}},"official":{"repos":["guangyliu/eddpm"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":13,"n_ran_no_instrument_failure":18,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/diffassemble-a-unified-graph-diffusion-model","slug":"diffassemble-a-unified-graph-diffusion-model","title":"DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly","date":"2024-02-29","arxiv_id":"2402.19302","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":2,"n_ran_checked":7,"n_instrument":1,"n_unverified":8,"n_honours":3,"n_violates":1,"n_no_contract":3,"n_pointer_only":16,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/diffassemble-a-unified-graph-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2402.19302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19302"}},"official":{"repos":["iit-pavis/diffassemble"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/listening-to-the-noise-blind-denoising-with","slug":"listening-to-the-noise-blind-denoising-with","title":"Listening to the Noise: Blind Denoising with Gibbs Diffusion","date":"2024-02-29","arxiv_id":"2402.19455","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/listening-to-the-noise-blind-denoising-with#ran","syntology_url":"https://syntology.ai/paper/2402.19455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19455"}},"official":{"repos":["rubenohana/gibbs-diffusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/purified-and-unified-steganographic-network","slug":"purified-and-unified-steganographic-network","title":"Purified and Unified Steganographic Network","date":"2024-02-27","arxiv_id":"2402.17210","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/purified-and-unified-steganographic-network#ran","syntology_url":"https://syntology.ai/paper/2402.17210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17210"}},"official":{"repos":["albblgb/pusnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/label-noise-robust-diffusion-models","slug":"label-noise-robust-diffusion-models","title":"Label-Noise Robust Diffusion Models","date":"2024-02-27","arxiv_id":"2402.17517","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/label-noise-robust-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2402.17517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17517"}},"official":{"repos":["byeonghu-na/tdsm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/diffusion-posterior-proximal-sampling-for","slug":"diffusion-posterior-proximal-sampling-for","title":"Diffusion Posterior Proximal Sampling for Image Restoration","date":"2024-02-25","arxiv_id":"2402.16907","repositories_listed":1,"syntology":{"n":26,"n_ran":15,"n_constructed":0,"n_ran_checked":9,"n_instrument":6,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":26,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/diffusion-posterior-proximal-sampling-for#ran","syntology_url":"https://syntology.ai/paper/2402.16907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16907"}},"official":{"repos":["74587887/dpps_code"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/hir-diff-unsupervised-hyperspectral-image","slug":"hir-diff-unsupervised-hyperspectral-image","title":"HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models","date":"2024-02-24","arxiv_id":"2402.15865","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hir-diff-unsupervised-hyperspectral-image#ran","syntology_url":"https://syntology.ai/paper/2402.15865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15865"}},"official":{"repos":["lipang/hirdiff"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/seamless-human-motion-composition-with","slug":"seamless-human-motion-composition-with","title":"Seamless Human Motion Composition with Blended Positional Encodings","date":"2024-02-23","arxiv_id":"2402.15509","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/seamless-human-motion-composition-with#ran","syntology_url":"https://syntology.ai/paper/2402.15509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15509"}},"official":{"repos":["BarqueroGerman/FlowMDM"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/geneoh-diffusion-towards-generalizable-hand","slug":"geneoh-diffusion-towards-generalizable-hand","title":"GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion","date":"2024-02-22","arxiv_id":"2402.14810","repositories_listed":1,"syntology":{"n":26,"n_ran":19,"n_constructed":0,"n_ran_checked":11,"n_instrument":8,"n_unverified":7,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":11,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 8 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/geneoh-diffusion-towards-generalizable-hand#ran","syntology_url":"https://syntology.ai/paper/2402.14810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14810"}},"official":{"repos":["meowuu7/geneoh-diffusion"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/cameras-as-rays-pose-estimation-via-ray","slug":"cameras-as-rays-pose-estimation-via-ray","title":"Cameras as Rays: Pose Estimation via Ray Diffusion","date":"2024-02-22","arxiv_id":"2402.14817","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cameras-as-rays-pose-estimation-via-ray#ran","syntology_url":"https://syntology.ai/paper/2402.14817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14817"}},"official":null}},{"url":"/paper/realcompo-dynamic-equilibrium-between-realism","slug":"realcompo-dynamic-equilibrium-between-realism","title":"RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models","date":"2024-02-20","arxiv_id":"2402.12908","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/realcompo-dynamic-equilibrium-between-realism#ran","syntology_url":"https://syntology.ai/paper/2402.12908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12908"}},"official":{"repos":["yangling0818/realcompo"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-style-prompting-with-swapping-self","slug":"visual-style-prompting-with-swapping-self","title":"Visual Style Prompting with Swapping Self-Attention","date":"2024-02-20","arxiv_id":"2402.12974","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visual-style-prompting-with-swapping-self#ran","syntology_url":"https://syntology.ai/paper/2402.12974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12974"}},"official":{"repos":["naver-ai/Visual-Style-Prompting"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-generative-pre-training-framework-for","slug":"a-generative-pre-training-framework-for","title":"Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation","date":"2024-02-19","arxiv_id":"2402.11922","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-generative-pre-training-framework-for#ran","syntology_url":"https://syntology.ai/paper/2402.11922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11922"}},"official":{"repos":["tsinghua-fib-lab/gpd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-generative-diffusion-models-via","slug":"explaining-generative-diffusion-models-via","title":"Explaining generative diffusion models via visual analysis for interpretable decision-making process","date":"2024-02-16","arxiv_id":"2402.10404","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/explaining-generative-diffusion-models-via#ran","syntology_url":"https://syntology.ai/paper/2402.10404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10404"}},"official":{"repos":["ian-jihoonpark/X-Diffusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/speaking-in-wavelet-domain-a-simple-and","slug":"speaking-in-wavelet-domain-a-simple-and","title":"Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model","date":"2024-02-16","arxiv_id":"2402.10642","repositories_listed":0,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/speaking-in-wavelet-domain-a-simple-and#ran","syntology_url":"https://syntology.ai/paper/2402.10642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10642"}},"official":null}},{"url":"/paper/stochastic-localization-via-iterative","slug":"stochastic-localization-via-iterative","title":"Stochastic Localization via Iterative Posterior Sampling","date":"2024-02-16","arxiv_id":"2402.10758","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-localization-via-iterative#ran","syntology_url":"https://syntology.ai/paper/2402.10758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10758"}},"official":{"repos":["h2o64/slips"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-diffuser-actor-policy-diffusion-with-3d-1","slug":"3d-diffuser-actor-policy-diffusion-with-3d-1","title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","date":"2024-02-16","arxiv_id":"2402.10885","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/3d-diffuser-actor-policy-diffusion-with-3d-1#ran","syntology_url":"https://syntology.ai/paper/2402.10885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10885"}},"official":null}},{"url":"/paper/dreammatcher-appearance-matching-self","slug":"dreammatcher-appearance-matching-self","title":"DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization","date":"2024-02-15","arxiv_id":"2402.09812","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dreammatcher-appearance-matching-self#ran","syntology_url":"https://syntology.ai/paper/2402.09812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09812"}},"official":{"repos":["KU-CVLAB/DreamMatcher"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/radio-astronomical-image-reconstruction-with","slug":"radio-astronomical-image-reconstruction-with","title":"Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model","date":"2024-02-15","arxiv_id":"2402.10204","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/radio-astronomical-image-reconstruction-with#ran","syntology_url":"https://syntology.ai/paper/2402.10204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10204"}},"official":{"repos":["mariiadrozdova/diffusion-for-sources-characterisation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-attempt-to-generate-new-bridge-types-from-5","slug":"an-attempt-to-generate-new-bridge-types-from-5","title":"An attempt to generate new bridge types from latent space of denoising diffusion Implicit model","date":"2024-02-11","arxiv_id":"2402.07129","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-attempt-to-generate-new-bridge-types-from-5#ran","syntology_url":"https://syntology.ai/paper/2402.07129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07129"}},"official":{"repos":["QQ583304953/Bridge-DDIM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/iterated-denoising-energy-matching-for","slug":"iterated-denoising-energy-matching-for","title":"Iterated Denoising Energy Matching for Sampling from Boltzmann Densities","date":"2024-02-09","arxiv_id":"2402.06121","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":3,"n_ran_checked":3,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/iterated-denoising-energy-matching-for#ran","syntology_url":"https://syntology.ai/paper/2402.06121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06121"}},"official":{"repos":["jarridrb/dem"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/particle-denoising-diffusion-sampler","slug":"particle-denoising-diffusion-sampler","title":"Particle Denoising Diffusion Sampler","date":"2024-02-09","arxiv_id":"2402.06320","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/particle-denoising-diffusion-sampler#ran","syntology_url":"https://syntology.ai/paper/2402.06320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06320"}},"official":{"repos":["angusphillips/particle_denoising_diffusion_sampler"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-es-gradient-free-planning-with","slug":"diffusion-es-gradient-free-planning-with","title":"Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following","date":"2024-02-09","arxiv_id":"2402.06559","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffusion-es-gradient-free-planning-with#ran","syntology_url":"https://syntology.ai/paper/2402.06559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06559"}},"official":{"repos":["bhyang/diffusion-es"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/time-series-diffusion-in-the-frequency-domain","slug":"time-series-diffusion-in-the-frequency-domain","title":"Time Series Diffusion in the Frequency Domain","date":"2024-02-08","arxiv_id":"2402.05933","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/time-series-diffusion-in-the-frequency-domain#ran","syntology_url":"https://syntology.ai/paper/2402.05933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05933"}},"official":{"repos":["jonathancrabbe/fourierdiffusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/social-physics-informed-diffusion-model-for","slug":"social-physics-informed-diffusion-model-for","title":"Social Physics Informed Diffusion Model for Crowd Simulation","date":"2024-02-08","arxiv_id":"2402.06680","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/social-physics-informed-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2402.06680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06680"}},"official":{"repos":["tsinghua-fib-lab/spdiff"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beam-beta-distribution-ray-denoising-for","slug":"beam-beta-distribution-ray-denoising-for","title":"Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection","date":"2024-02-06","arxiv_id":"2402.03634","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/beam-beta-distribution-ray-denoising-for#ran","syntology_url":"https://syntology.ai/paper/2402.03634","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03634"}},"official":{"repos":["liewfeng/beam","liewfeng/raydn"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-and-unifying-discrete-continuous","slug":"improving-and-unifying-discrete-continuous","title":"Unified Discrete Diffusion for Categorical Data","date":"2024-02-06","arxiv_id":"2402.03701","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-and-unifying-discrete-continuous#ran","syntology_url":"https://syntology.ai/paper/2402.03701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03701"}},"official":{"repos":["lingxiaoshawn/usd3"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"c2cde4c02efc16272bc1097f6316e945c81c2b2d44bc030b909e3e24c5278553","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}