{"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/image-generation/papers/5","list_of":"/task/image-generation","task":"Image Generation","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":67,"rows_per_page":100,"rows":[401,500],"of":6689,"counts":{"archive_papers_tagged":6689,"with_a_code_link":3102,"where_syntology_ran_a_sample":1223,"not_listed_spam_title":0,"listed":6689,"listed_where_code_ran":1223,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1063,"every_run_a_failure_of_syntologys_instrument":160,"listed_with_a_run_with_no_instrument_failure":1063,"listed_every_run_a_failure_of_syntologys_instrument":160,"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/image-generation","prev":"/task/image-generation/papers/4","next":"/task/image-generation/papers/6","papers":[{"url":"/paper/tr0n-translator-networks-for-0-shot-plug-and","slug":"tr0n-translator-networks-for-0-shot-plug-and","title":"TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation","date":"2023-04-26","arxiv_id":"2304.13742","repositories_listed":2,"syntology":{"n":19,"n_ran":17,"n_constructed":1,"n_ran_checked":12,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":9,"phrase":"17 ran (of which 1 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tr0n-translator-networks-for-0-shot-plug-and#ran","syntology_url":"https://syntology.ai/paper/2304.13742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13742"}},"official":{"repos":["layer6ai-labs/tr0n"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/dcn-t-dual-context-network-with-transformer","slug":"dcn-t-dual-context-network-with-transformer","title":"DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification","date":"2023-04-19","arxiv_id":"2304.09915","repositories_listed":2,"syntology":null},{"url":"/paper/diagnostic-benchmark-and-iterative-inpainting","slug":"diagnostic-benchmark-and-iterative-inpainting","title":"Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation","date":"2023-04-13","arxiv_id":"2304.06671","repositories_listed":2,"syntology":null},{"url":"/paper/patmat-person-aware-tuning-of-mask-aware","slug":"patmat-person-aware-tuning-of-mask-aware","title":"PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting","date":"2023-04-12","arxiv_id":"2304.06107","repositories_listed":2,"syntology":null},{"url":"/paper/follow-your-pose-pose-guided-text-to-video","slug":"follow-your-pose-pose-guided-text-to-video","title":"Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos","date":"2023-04-03","arxiv_id":"2304.01186","repositories_listed":2,"syntology":{"n":10,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/follow-your-pose-pose-guided-text-to-video#ran","syntology_url":"https://syntology.ai/paper/2304.01186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01186"}},"official":{"repos":["mayuelala/followyourpose"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/layoutdiffusion-controllable-diffusion-model","slug":"layoutdiffusion-controllable-diffusion-model","title":"LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation","date":"2023-03-30","arxiv_id":"2303.17189","repositories_listed":2,"syntology":{"n":19,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":8,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/layoutdiffusion-controllable-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2303.17189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17189"}},"official":{"repos":["dcdcvgroup/layout-diffusion-mindspore"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/instant-neural-radiance-fields-stylization","slug":"instant-neural-radiance-fields-stylization","title":"Instant Photorealistic Neural Radiance Fields Stylization","date":"2023-03-29","arxiv_id":"2303.16884","repositories_listed":2,"syntology":null},{"url":"/paper/mdp-a-generalized-framework-for-text-guided","slug":"mdp-a-generalized-framework-for-text-guided","title":"MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path","date":"2023-03-29","arxiv_id":"2303.16765","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mdp-a-generalized-framework-for-text-guided#ran","syntology_url":"https://syntology.ai/paper/2303.16765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16765"}},"official":{"repos":["qianwangx/mdp-diffusion"],"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":["listed","official"]}}},{"url":"/paper/high-fidelity-image-synthesis-with-deep-vaes","slug":"high-fidelity-image-synthesis-with-deep-vaes","title":"High Fidelity Image Synthesis With Deep VAEs In Latent Space","date":"2023-03-23","arxiv_id":"2303.13714","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/high-fidelity-image-synthesis-with-deep-vaes#ran","syntology_url":"https://syntology.ai/paper/2303.13714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13714"}},"official":{"repos":["ericl122333/latent-vae","ericl122333/latent-vae-jax"],"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/discovering-interpretable-directions-in-the","slug":"discovering-interpretable-directions-in-the","title":"Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models","date":"2023-03-20","arxiv_id":"2303.11073","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discovering-interpretable-directions-in-the#ran","syntology_url":"https://syntology.ai/paper/2303.11073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11073"}},"official":{"repos":["renhaa/semantic-diffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/object-centric-slot-diffusion-1","slug":"object-centric-slot-diffusion-1","title":"Object-Centric Slot Diffusion","date":"2023-03-20","arxiv_id":"2303.10834","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"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) · 3 unverified","sample_list":"/paper/object-centric-slot-diffusion-1#ran","syntology_url":"https://syntology.ai/paper/2303.10834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10834"}},"official":{"repos":["jindongjiang/latent-slot-diffusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-diffusion-training-via-min-snr","slug":"efficient-diffusion-training-via-min-snr","title":"Efficient Diffusion Training via Min-SNR Weighting Strategy","date":"2023-03-16","arxiv_id":"2303.09556","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-diffusion-training-via-min-snr#ran","syntology_url":"https://syntology.ai/paper/2303.09556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09556"}},"official":{"repos":["tiankaihang/min-snr-diffusion-training"],"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":["listed","official","unlocated"]}}},{"url":"/paper/decomposed-diffusion-models-for-high-quality","slug":"decomposed-diffusion-models-for-high-quality","title":"VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation","date":"2023-03-15","arxiv_id":"2303.08320","repositories_listed":2,"syntology":null},{"url":"/paper/new-benchmarks-for-accountable-text-based","slug":"new-benchmarks-for-accountable-text-based","title":"Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation","date":"2023-03-10","arxiv_id":"2303.05983","repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-one-shot-learning-for","slug":"self-supervised-one-shot-learning-for","title":"Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images","date":"2023-03-10","arxiv_id":"2303.05639","repositories_listed":2,"syntology":null},{"url":"/paper/vector-quantized-time-series-generation-with","slug":"vector-quantized-time-series-generation-with","title":"Vector Quantized Time Series Generation with a Bidirectional Prior Model","date":"2023-03-08","arxiv_id":"2303.04743","repositories_listed":2,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":13,"n_pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 2 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vector-quantized-time-series-generation-with#ran","syntology_url":"https://syntology.ai/paper/2303.04743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.04743"}},"official":{"repos":["danelee2601/supervised-fcn","ml4its/timevqvae"],"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/elite-encoding-visual-concepts-into-textual","slug":"elite-encoding-visual-concepts-into-textual","title":"ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation","date":"2023-02-27","arxiv_id":"2302.13848","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":1,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 1 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/elite-encoding-visual-concepts-into-textual#ran","syntology_url":"https://syntology.ai/paper/2302.13848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.13848"}},"official":{"repos":["csyxwei/elite"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/3d-aware-conditional-image-synthesis","slug":"3d-aware-conditional-image-synthesis","title":"3D-aware Conditional Image Synthesis","date":"2023-02-16","arxiv_id":"2302.08509","repositories_listed":2,"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/3d-aware-conditional-image-synthesis#ran","syntology_url":"https://syntology.ai/paper/2302.08509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08509"}},"official":{"repos":["dunbar12138/pix2pix3d"],"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/t2i-adapter-learning-adapters-to-dig-out-more","slug":"t2i-adapter-learning-adapters-to-dig-out-more","title":"T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models","date":"2023-02-16","arxiv_id":"2302.08453","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/t2i-adapter-learning-adapters-to-dig-out-more#ran","syntology_url":"https://syntology.ai/paper/2302.08453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08453"}},"official":{"repos":["tencentarc/t2i-adapter"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/masksketch-unpaired-structure-guided-masked","slug":"masksketch-unpaired-structure-guided-masked","title":"MaskSketch: Unpaired Structure-guided Masked Image Generation","date":"2023-02-10","arxiv_id":"2302.05496","repositories_listed":2,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/masksketch-unpaired-structure-guided-masked#ran","syntology_url":"https://syntology.ai/paper/2302.05496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05496"}},"official":{"repos":["google-research/masksketch"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/med-nca-robust-and-lightweight-segmentation","slug":"med-nca-robust-and-lightweight-segmentation","title":"Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata","date":"2023-02-07","arxiv_id":"2302.03473","repositories_listed":2,"syntology":null},{"url":"/paper/texture-text-guided-texturing-of-3d-shapes","slug":"texture-text-guided-texturing-of-3d-shapes","title":"TEXTure: Text-Guided Texturing of 3D Shapes","date":"2023-02-03","arxiv_id":"2302.01721","repositories_listed":2,"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/texture-text-guided-texturing-of-3d-shapes#ran","syntology_url":"https://syntology.ai/paper/2302.01721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01721"}},"official":{"repos":["TEXTurePaper/TEXTurePaper"],"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/are-diffusion-models-vulnerable-to-membership","slug":"are-diffusion-models-vulnerable-to-membership","title":"Are Diffusion Models Vulnerable to Membership Inference Attacks?","date":"2023-02-02","arxiv_id":"2302.01316","repositories_listed":2,"syntology":{"n":23,"n_ran":11,"n_constructed":0,"n_ran_checked":4,"n_instrument":7,"n_unverified":12,"n_honours":2,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 7 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/are-diffusion-models-vulnerable-to-membership#ran","syntology_url":"https://syntology.ai/paper/2302.01316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01316"}},"official":{"repos":["jinhaoduan/secmi"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/galip-generative-adversarial-clips-for-text","slug":"galip-generative-adversarial-clips-for-text","title":"GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis","date":"2023-01-30","arxiv_id":"2301.12959","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/galip-generative-adversarial-clips-for-text#ran","syntology_url":"https://syntology.ai/paper/2301.12959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12959"}},"official":{"repos":["tobran/galip"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/medsegdiff-v2-diffusion-based-medical-image","slug":"medsegdiff-v2-diffusion-based-medical-image","title":"MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer","date":"2023-01-19","arxiv_id":"2301.11798","repositories_listed":2,"syntology":{"n":21,"n_ran":17,"n_constructed":0,"n_ran_checked":12,"n_instrument":5,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":10,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/medsegdiff-v2-diffusion-based-medical-image#ran","syntology_url":"https://syntology.ai/paper/2301.11798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11798"}},"official":{"repos":["kidswithtokens/medsegdiff","wujunde/medsegdiff"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-adaptive-computation-for-iterative","slug":"scalable-adaptive-computation-for-iterative","title":"Scalable Adaptive Computation for Iterative Generation","date":"2022-12-22","arxiv_id":"2212.11972","repositories_listed":2,"syntology":{"n":29,"n_ran":21,"n_constructed":9,"n_ran_checked":18,"n_instrument":3,"n_unverified":8,"n_honours":8,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"21 ran (of which 9 constructed an object rather than computing a result; 18 with no instrument failure: 8 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/scalable-adaptive-computation-for-iterative#ran","syntology_url":"https://syntology.ai/paper/2212.11972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.11972"}},"official":{"repos":["google-research/pix2seq"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":2,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/the-stable-artist-steering-semantics-in","slug":"the-stable-artist-steering-semantics-in","title":"The Stable Artist: Steering Semantics in Diffusion Latent Space","date":"2022-12-12","arxiv_id":"2212.06013","repositories_listed":2,"syntology":null},{"url":"/paper/semantic-conditional-diffusion-networks-for","slug":"semantic-conditional-diffusion-networks-for","title":"Semantic-Conditional Diffusion Networks for Image Captioning","date":"2022-12-06","arxiv_id":"2212.03099","repositories_listed":2,"syntology":null},{"url":"/paper/slmt-net-a-self-supervised-learning-based","slug":"slmt-net-a-self-supervised-learning-based","title":"Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis","date":"2022-12-02","arxiv_id":"2212.01108","repositories_listed":2,"syntology":null},{"url":"/paper/refining-generative-process-with","slug":"refining-generative-process-with","title":"Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models","date":"2022-11-28","arxiv_id":"2211.17091","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/refining-generative-process-with#ran","syntology_url":"https://syntology.ai/paper/2211.17091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.17091"}},"official":{"repos":["alsdudrla10/DG","alsdudrla10/DG_imagenet"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/paint-by-example-exemplar-based-image-editing","slug":"paint-by-example-exemplar-based-image-editing","title":"Paint by Example: Exemplar-based Image Editing with Diffusion Models","date":"2022-11-23","arxiv_id":"2211.13227","repositories_listed":2,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":15,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":13,"n_pointer_only":4,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 2 violated, 13 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/paint-by-example-exemplar-based-image-editing#ran","syntology_url":"https://syntology.ai/paper/2211.13227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13227"}},"official":{"repos":["Fantasy-Studio/Paint-by-Example"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/latent-nerf-for-shape-guided-generation-of-3d","slug":"latent-nerf-for-shape-guided-generation-of-3d","title":"Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures","date":"2022-11-14","arxiv_id":"2211.07600","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/latent-nerf-for-shape-guided-generation-of-3d#ran","syntology_url":"https://syntology.ai/paper/2211.07600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07600"}},"official":{"repos":["eladrich/latent-nerf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/stylenat-giving-each-head-a-new-perspective","slug":"stylenat-giving-each-head-a-new-perspective","title":"StyleNAT: Giving Each Head a New Perspective","date":"2022-11-10","arxiv_id":"2211.05770","repositories_listed":2,"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":0,"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/stylenat-giving-each-head-a-new-perspective#ran","syntology_url":"https://syntology.ai/paper/2211.05770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.05770"}},"official":{"repos":["SHI-Labs/StyleNAT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/safe-latent-diffusion-mitigating","slug":"safe-latent-diffusion-mitigating","title":"Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models","date":"2022-11-09","arxiv_id":"2211.05105","repositories_listed":2,"syntology":null},{"url":"/paper/ediffi-text-to-image-diffusion-models-with-an","slug":"ediffi-text-to-image-diffusion-models-with-an","title":"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers","date":"2022-11-02","arxiv_id":"2211.01324","repositories_listed":2,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ediffi-text-to-image-diffusion-models-with-an#ran","syntology_url":"https://syntology.ai/paper/2211.01324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01324"}},"official":null}},{"url":"/paper/medsegdiff-medical-image-segmentation-with","slug":"medsegdiff-medical-image-segmentation-with","title":"MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model","date":"2022-11-01","arxiv_id":"2211.00611","repositories_listed":2,"syntology":null},{"url":"/paper/few-shot-image-generation-via-adaptation","slug":"few-shot-image-generation-via-adaptation","title":"Few-shot Image Generation via Adaptation-Aware Kernel Modulation","date":"2022-10-29","arxiv_id":"2210.16559","repositories_listed":2,"syntology":{"n":23,"n_ran":17,"n_constructed":0,"n_ran_checked":14,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":13,"n_pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 1 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/few-shot-image-generation-via-adaptation#ran","syntology_url":"https://syntology.ai/paper/2210.16559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16559"}},"official":{"repos":["yunqing-me/AdAM"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/ernie-vilg-2-0-improving-text-to-image","slug":"ernie-vilg-2-0-improving-text-to-image","title":"ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts","date":"2022-10-27","arxiv_id":"2210.15257","repositories_listed":2,"syntology":null},{"url":"/paper/towards-the-detection-of-diffusion-model","slug":"towards-the-detection-of-diffusion-model","title":"Towards the Detection of Diffusion Model Deepfakes","date":"2022-10-26","arxiv_id":"2210.14571","repositories_listed":2,"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/towards-the-detection-of-diffusion-model#ran","syntology_url":"https://syntology.ai/paper/2210.14571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14571"}},"official":{"repos":["jonasricker/diffusion-model-deepfake-detection"],"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/character-centric-story-visualization-via","slug":"character-centric-story-visualization-via","title":"Character-Centric Story Visualization via Visual Planning and Token Alignment","date":"2022-10-16","arxiv_id":"2210.08465","repositories_listed":2,"syntology":{"n":21,"n_ran":12,"n_constructed":7,"n_ran_checked":9,"n_instrument":3,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/character-centric-story-visualization-via#ran","syntology_url":"https://syntology.ai/paper/2210.08465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08465"}},"official":{"repos":["pluslabnlp/vp-csv","sairin1202/vp-csv"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/what-the-daam-interpreting-stable-diffusion","slug":"what-the-daam-interpreting-stable-diffusion","title":"What the DAAM: Interpreting Stable Diffusion Using Cross Attention","date":"2022-10-10","arxiv_id":"2210.04885","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/what-the-daam-interpreting-stable-diffusion#ran","syntology_url":"https://syntology.ai/paper/2210.04885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04885"}},"official":{"repos":["castorini/daam"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/on-distillation-of-guided-diffusion-models","slug":"on-distillation-of-guided-diffusion-models","title":"On Distillation of Guided Diffusion Models","date":"2022-10-06","arxiv_id":"2210.03142","repositories_listed":2,"syntology":null},{"url":"/paper/make-a-video-text-to-video-generation-without","slug":"make-a-video-text-to-video-generation-without","title":"Make-A-Video: Text-to-Video Generation without Text-Video Data","date":"2022-09-29","arxiv_id":"2209.14792","repositories_listed":2,"syntology":null},{"url":"/paper/movq-modulating-quantized-vectors-for-high","slug":"movq-modulating-quantized-vectors-for-high","title":"MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation","date":"2022-09-19","arxiv_id":"2209.09002","repositories_listed":2,"syntology":null},{"url":"/paper/the-biased-artist-exploiting-cultural-biases","slug":"the-biased-artist-exploiting-cultural-biases","title":"Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis","date":"2022-09-19","arxiv_id":"2209.08891","repositories_listed":2,"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/the-biased-artist-exploiting-cultural-biases#ran","syntology_url":"https://syntology.ai/paper/2209.08891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.08891"}},"official":{"repos":["lukasstruppek/exploiting-cultural-biases-via-homoglyphs","lukasstruppek/the-biased-artist"],"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/diffusion-models-a-comprehensive-survey-of","slug":"diffusion-models-a-comprehensive-survey-of","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","date":"2022-09-02","arxiv_id":"2209.00796","repositories_listed":2,"syntology":null},{"url":"/paper/your-vit-is-secretly-a-hybrid-discriminative","slug":"your-vit-is-secretly-a-hybrid-discriminative","title":"Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model","date":"2022-08-16","arxiv_id":"2208.07791","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/your-vit-is-secretly-a-hybrid-discriminative#ran","syntology_url":"https://syntology.ai/paper/2208.07791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.07791"}},"official":{"repos":["sndnyang/Diffusion_ViT"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/seamless-iterative-semi-supervised-correction","slug":"seamless-iterative-semi-supervised-correction","title":"Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images","date":"2022-08-05","arxiv_id":"2208.03327","repositories_listed":2,"syntology":null},{"url":"/paper/unsupervised-medical-image-translation-with","slug":"unsupervised-medical-image-translation-with","title":"Unsupervised Medical Image Translation with Adversarial Diffusion Models","date":"2022-07-17","arxiv_id":"2207.08208","repositories_listed":2,"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/unsupervised-medical-image-translation-with#ran","syntology_url":"https://syntology.ai/paper/2207.08208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08208"}},"official":{"repos":["icon-lab/syndiff"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/scaling-autoregressive-models-for-content","slug":"scaling-autoregressive-models-for-content","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","date":"2022-06-22","arxiv_id":"2206.10789","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scaling-autoregressive-models-for-content#ran","syntology_url":"https://syntology.ai/paper/2206.10789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10789"}},"official":null}},{"url":"/paper/studiogan-a-taxonomy-and-benchmark-of-gans","slug":"studiogan-a-taxonomy-and-benchmark-of-gans","title":"StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis","date":"2022-06-19","arxiv_id":"2206.09479","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":2,"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/studiogan-a-taxonomy-and-benchmark-of-gans#ran","syntology_url":"https://syntology.ai/paper/2206.09479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09479"}},"official":{"repos":["POSTECH-CVLab/PyTorch-StudioGAN"],"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":["listed","official"]}}},{"url":"/paper/from-keypoints-to-object-landmarks-via-self","slug":"from-keypoints-to-object-landmarks-via-self","title":"From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery","date":"2022-05-31","arxiv_id":"2205.15895","repositories_listed":2,"syntology":null},{"url":"/paper/text2human-text-driven-controllable-human","slug":"text2human-text-driven-controllable-human","title":"Text2Human: Text-Driven Controllable Human Image Generation","date":"2022-05-31","arxiv_id":"2205.15996","repositories_listed":2,"syntology":null},{"url":"/paper/vqbb-image-to-image-translation-with-vector","slug":"vqbb-image-to-image-translation-with-vector","title":"BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models","date":"2022-05-16","arxiv_id":"2205.07680","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":6,"n_pointer_only":0,"phrase":"10 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/vqbb-image-to-image-translation-with-vector#ran","syntology_url":"https://syntology.ai/paper/2205.07680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07680"}},"official":{"repos":["xuekt98/bbdm"],"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":["listed","official"]}}},{"url":"/paper/deep-pcb-to-coco-convertor","slug":"deep-pcb-to-coco-convertor","title":"Deep PCB To COCO Convertor","date":"2022-05-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/retrieval-augmented-diffusion-models","slug":"retrieval-augmented-diffusion-models","title":"Semi-Parametric Neural Image Synthesis","date":"2022-04-25","arxiv_id":"2204.11824","repositories_listed":2,"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/retrieval-augmented-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2204.11824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.11824"}},"official":{"repos":["compvis/latent-diffusion","lucidrains/retrieval-augmented-ddpm"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/learning-to-generate-realistic-noisy-images-2","slug":"learning-to-generate-realistic-noisy-images-2","title":"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training","date":"2022-04-06","arxiv_id":"2204.02844","repositories_listed":2,"syntology":{"n":18,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-to-generate-realistic-noisy-images-2#ran","syntology_url":"https://syntology.ai/paper/2204.02844","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02844"}},"official":null}},{"url":"/paper/denoising-likelihood-score-matching-for-1","slug":"denoising-likelihood-score-matching-for-1","title":"Denoising Likelihood Score Matching for Conditional Score-based Data Generation","date":"2022-03-27","arxiv_id":"2203.14206","repositories_listed":2,"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":0,"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/denoising-likelihood-score-matching-for-1#ran","syntology_url":"https://syntology.ai/paper/2203.14206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14206"}},"official":{"repos":["chen-hao-chao/dlsm"],"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":["listed","official"]}}},{"url":"/paper/which-generative-adversarial-network-yields","slug":"which-generative-adversarial-network-yields","title":"Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization","date":"2022-03-25","arxiv_id":"2203.13856","repositories_listed":2,"syntology":null},{"url":"/paper/generating-natural-images-with-direct-patch","slug":"generating-natural-images-with-direct-patch","title":"Generating natural images with direct Patch Distributions Matching","date":"2022-03-22","arxiv_id":"2203.11862","repositories_listed":2,"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/generating-natural-images-with-direct-patch#ran","syntology_url":"https://syntology.ai/paper/2203.11862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11862"}},"official":{"repos":["ariel415el/Efficient-GPNN","ariel415el/gpdm"],"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/insetgan-for-full-body-image-generation","slug":"insetgan-for-full-body-image-generation","title":"InsetGAN for Full-Body Image Generation","date":"2022-03-14","arxiv_id":"2203.07293","repositories_listed":2,"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/insetgan-for-full-body-image-generation#ran","syntology_url":"https://syntology.ai/paper/2203.07293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07293"}},"official":{"repos":["afruehstueck/insetGAN"],"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/petsgan-rethinking-priors-for-single-image","slug":"petsgan-rethinking-priors-for-single-image","title":"PetsGAN: Rethinking Priors for Single Image Generation","date":"2022-03-03","arxiv_id":"2203.01488","repositories_listed":2,"syntology":null},{"url":"/paper/clip-gen-language-free-training-of-a-text-to","slug":"clip-gen-language-free-training-of-a-text-to","title":"CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP","date":"2022-03-01","arxiv_id":"2203.00386","repositories_listed":2,"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/clip-gen-language-free-training-of-a-text-to#ran","syntology_url":"https://syntology.ai/paper/2203.00386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00386"}},"official":null}},{"url":"/paper/pix2nerf-unsupervised-conditional-p-gan-for","slug":"pix2nerf-unsupervised-conditional-p-gan-for","title":"Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation","date":"2022-02-26","arxiv_id":"2202.13162","repositories_listed":2,"syntology":null},{"url":"/paper/self-distilled-stylegan-towards-generation","slug":"self-distilled-stylegan-towards-generation","title":"Self-Distilled StyleGAN: Towards Generation from Internet Photos","date":"2022-02-24","arxiv_id":"2202.12211","repositories_listed":2,"syntology":null},{"url":"/paper/dall-eval-probing-the-reasoning-skills-and","slug":"dall-eval-probing-the-reasoning-skills-and","title":"DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models","date":"2022-02-08","arxiv_id":"2202.04053","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dall-eval-probing-the-reasoning-skills-and#ran","syntology_url":"https://syntology.ai/paper/2202.04053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.04053"}},"official":{"repos":["j-min/dalleval"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/stylegan-xl-scaling-stylegan-to-large-diverse","slug":"stylegan-xl-scaling-stylegan-to-large-diverse","title":"StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets","date":"2022-02-01","arxiv_id":"2202.00273","repositories_listed":2,"syntology":{"n":19,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":3,"n_honours":5,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 5 honoured, 0 violated, 5 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/stylegan-xl-scaling-stylegan-to-large-diverse#ran","syntology_url":"https://syntology.ai/paper/2202.00273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.00273"}},"official":{"repos":["autonomousvision/stylegan_xl"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-issues-of-truedepth-sensor-data-for","slug":"on-the-issues-of-truedepth-sensor-data-for","title":"On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations","date":"2022-01-26","arxiv_id":"2201.10865","repositories_listed":2,"syntology":null},{"url":"/paper/ernie-vilg-unified-generative-pre-training","slug":"ernie-vilg-unified-generative-pre-training","title":"ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation","date":"2021-12-31","arxiv_id":"2112.15283","repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-image-synthesis-and-editing-a","slug":"multimodal-image-synthesis-and-editing-a","title":"Multimodal Image Synthesis and Editing: The Generative AI Era","date":"2021-12-27","arxiv_id":"2112.13592","repositories_listed":2,"syntology":null},{"url":"/paper/glide-towards-photorealistic-image-generation","slug":"glide-towards-photorealistic-image-generation","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","date":"2021-12-20","arxiv_id":"2112.10741","repositories_listed":2,"syntology":{"n":15,"n_ran":9,"n_constructed":7,"n_ran_checked":8,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/glide-towards-photorealistic-image-generation#ran","syntology_url":"https://syntology.ai/paper/2112.10741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10741"}},"official":{"repos":["openai/glide-text2im"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/translation-equivariant-image-quantizer-for","slug":"translation-equivariant-image-quantizer-for","title":"Exploration into Translation-Equivariant Image Quantization","date":"2021-12-01","arxiv_id":"2112.00384","repositories_listed":2,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":4,"n_instrument":9,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"13 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; 9 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/translation-equivariant-image-quantizer-for#ran","syntology_url":"https://syntology.ai/paper/2112.00384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.00384"}},"official":{"repos":["wcshin-git/te-vqgan"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vector-quantized-diffusion-model-for-text-to","slug":"vector-quantized-diffusion-model-for-text-to","title":"Vector Quantized Diffusion Model for Text-to-Image Synthesis","date":"2021-11-29","arxiv_id":"2111.14822","repositories_listed":2,"syntology":null},{"url":"/paper/realistic-galaxy-image-simulation-via-score","slug":"realistic-galaxy-image-simulation-via-score","title":"Realistic galaxy image simulation via score-based generative models","date":"2021-11-02","arxiv_id":"2111.01713","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/realistic-galaxy-image-simulation-via-score#ran","syntology_url":"https://syntology.ai/paper/2111.01713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.01713"}},"official":{"repos":["Smith42/astroddpm","smith42/synthetic-galaxy-distance"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/deepvecfont-synthesizing-high-quality-vector","slug":"deepvecfont-synthesizing-high-quality-vector","title":"DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning","date":"2021-10-13","arxiv_id":"2110.06688","repositories_listed":2,"syntology":null},{"url":"/paper/the-deep-generative-decoder-using-map-1","slug":"the-deep-generative-decoder-using-map-1","title":"The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data","date":"2021-10-13","arxiv_id":"2110.06672","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":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) · 0 unverified","sample_list":"/paper/the-deep-generative-decoder-using-map-1#ran","syntology_url":"https://syntology.ai/paper/2110.06672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06672"}},"official":{"repos":["Center-for-Health-Data-Science/scDGD"],"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":["named_in_paper","official"]}}},{"url":"/paper/climategan-raising-climate-change-awareness","slug":"climategan-raising-climate-change-awareness","title":"ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods","date":"2021-10-06","arxiv_id":"2110.02871","repositories_listed":2,"syntology":null},{"url":"/paper/generative-modeling-with-optimal-transport","slug":"generative-modeling-with-optimal-transport","title":"Generative Modeling with Optimal Transport Maps","date":"2021-10-06","arxiv_id":"2110.02999","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generative-modeling-with-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/2110.02999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02999"}},"official":{"repos":["LituRout/OptimalTransportModeling"],"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/toward-spatially-unbiased-generative-models","slug":"toward-spatially-unbiased-generative-models","title":"Toward Spatially Unbiased Generative Models","date":"2021-08-03","arxiv_id":"2108.01285","repositories_listed":2,"syntology":null},{"url":"/paper/sdedit-image-synthesis-and-editing-with","slug":"sdedit-image-synthesis-and-editing-with","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","date":"2021-08-02","arxiv_id":"2108.01073","repositories_listed":2,"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":0,"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/sdedit-image-synthesis-and-editing-with#ran","syntology_url":"https://syntology.ai/paper/2108.01073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01073"}},"official":{"repos":["ermongroup/SDEdit"],"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/dcl-differential-contrastive-learning-for","slug":"dcl-differential-contrastive-learning-for","title":"DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis","date":"2021-07-27","arxiv_id":"2107.13087","repositories_listed":2,"syntology":null},{"url":"/paper/combiner-full-attention-transformer-with","slug":"combiner-full-attention-transformer-with","title":"Combiner: Full Attention Transformer with Sparse Computation Cost","date":"2021-07-12","arxiv_id":"2107.05768","repositories_listed":2,"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/combiner-full-attention-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2107.05768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05768"}},"official":{"repos":["google-research/google-research"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bi-level-feature-alignment-for-versatile","slug":"bi-level-feature-alignment-for-versatile","title":"Bi-level Feature Alignment for Versatile Image Translation and Manipulation","date":"2021-07-07","arxiv_id":"2107.03021","repositories_listed":2,"syntology":null},{"url":"/paper/resvit-residual-vision-transformers-for-multi","slug":"resvit-residual-vision-transformers-for-multi","title":"ResViT: Residual vision transformers for multi-modal medical image synthesis","date":"2021-06-30","arxiv_id":"2106.16031","repositories_listed":2,"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/resvit-residual-vision-transformers-for-multi#ran","syntology_url":"https://syntology.ai/paper/2106.16031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.16031"}},"official":{"repos":["icon-lab/ResViT"],"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/single-image-texture-translation-for-data","slug":"single-image-texture-translation-for-data","title":"SITTA: Single Image Texture Translation for Data Augmentation","date":"2021-06-25","arxiv_id":"2106.13804","repositories_listed":2,"syntology":null},{"url":"/paper/feature-alignment-for-approximated","slug":"feature-alignment-for-approximated","title":"Feature Alignment as a Generative Process","date":"2021-06-23","arxiv_id":"2106.12562","repositories_listed":2,"syntology":null},{"url":"/paper/adversarial-manifold-matching-via-deep-metric","slug":"adversarial-manifold-matching-via-deep-metric","title":"Manifold Matching via Deep Metric Learning for Generative Modeling","date":"2021-06-20","arxiv_id":"2106.10777","repositories_listed":2,"syntology":null},{"url":"/paper/adaptive-convolutions-for-structure-aware","slug":"adaptive-convolutions-for-structure-aware","title":"Adaptive Convolutions for Structure-Aware Style Transfer","date":"2021-06-19","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-gans-with-label-augmentation","slug":"self-supervised-gans-with-label-augmentation","title":"Self-Supervised GANs with Label Augmentation","date":"2021-06-16","arxiv_id":"2106.08601","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-gans-with-label-augmentation#ran","syntology_url":"https://syntology.ai/paper/2106.08601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08601"}},"official":{"repos":["houliangict/ssgan-la"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/do-not-escape-from-the-manifold-discovering","slug":"do-not-escape-from-the-manifold-discovering","title":"Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs","date":"2021-06-13","arxiv_id":"2106.06959","repositories_listed":2,"syntology":null},{"url":"/paper/multiresolution-graph-variational-autoencoder","slug":"multiresolution-graph-variational-autoencoder","title":"Multiresolution Equivariant Graph Variational Autoencoder","date":"2021-06-02","arxiv_id":"2106.00967","repositories_listed":2,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/multiresolution-graph-variational-autoencoder#ran","syntology_url":"https://syntology.ai/paper/2106.00967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00967"}},"official":{"repos":["hytruongson/mgvae"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/nvisii-a-scriptable-tool-for-photorealistic","slug":"nvisii-a-scriptable-tool-for-photorealistic","title":"NViSII: A Scriptable Tool for Photorealistic Image Generation","date":"2021-05-28","arxiv_id":"2105.13962","repositories_listed":2,"syntology":null},{"url":"/paper/neurogen-activation-optimized-image-synthesis","slug":"neurogen-activation-optimized-image-synthesis","title":"NeuroGen: activation optimized image synthesis for discovery neuroscience","date":"2021-05-15","arxiv_id":"2105.07140","repositories_listed":2,"syntology":null},{"url":"/paper/genesis-v2-inferring-unordered-object","slug":"genesis-v2-inferring-unordered-object","title":"GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement","date":"2021-04-20","arxiv_id":"2104.09958","repositories_listed":2,"syntology":null},{"url":"/paper/towards-open-world-text-guided-face-image","slug":"towards-open-world-text-guided-face-image","title":"Towards Open-World Text-Guided Face Image Generation and Manipulation","date":"2021-04-18","arxiv_id":"2104.08910","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-open-world-text-guided-face-image#ran","syntology_url":"https://syntology.ai/paper/2104.08910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08910"}},"official":{"repos":["weihaox/TediGAN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/vtgan-semi-supervised-retinal-image-synthesis","slug":"vtgan-semi-supervised-retinal-image-synthesis","title":"VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers","date":"2021-04-14","arxiv_id":"2104.06757","repositories_listed":2,"syntology":null},{"url":"/paper/neural-rgb-d-surface-reconstruction","slug":"neural-rgb-d-surface-reconstruction","title":"Neural RGB-D Surface Reconstruction","date":"2021-04-09","arxiv_id":"2104.04532","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/neural-rgb-d-surface-reconstruction#ran","syntology_url":"https://syntology.ai/paper/2104.04532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.04532"}},"official":{"repos":["dazinovic/neural-rgbd-surface-reconstruction"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/restyle-a-residual-based-stylegan-encoder-via","slug":"restyle-a-residual-based-stylegan-encoder-via","title":"ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement","date":"2021-04-06","arxiv_id":"2104.02699","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/restyle-a-residual-based-stylegan-encoder-via#ran","syntology_url":"https://syntology.ai/paper/2104.02699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02699"}},"official":{"repos":["yuval-alaluf/restyle-encoder"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/latentclr-a-contrastive-learning-approach-for","slug":"latentclr-a-contrastive-learning-approach-for","title":"LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions","date":"2021-04-02","arxiv_id":"2104.00820","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/latentclr-a-contrastive-learning-approach-for#ran","syntology_url":"https://syntology.ai/paper/2104.00820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00820"}},"official":{"repos":["catlab-team/latentclr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/drop-the-gan-in-defense-of-patches-nearest","slug":"drop-the-gan-in-defense-of-patches-nearest","title":"Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models","date":"2021-03-29","arxiv_id":"2103.15545","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/drop-the-gan-in-defense-of-patches-nearest#ran","syntology_url":"https://syntology.ai/paper/2103.15545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15545"}},"official":null}}],"record_sha256":"d9c21615c5ec95cf281b369f03be4671cfb36e777563cfea6881ca19e5e2bbff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}