{"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/14","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":14,"pages_in_order":73,"rows_per_page":100,"rows":[1301,1400],"of":7282,"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","prev":"/task/denoising/papers/13","next":"/task/denoising/papers/15","papers":[{"url":"/paper/high-quality-image-restoration-following","slug":"high-quality-image-restoration-following","title":"InstructIR: High-Quality Image Restoration Following Human Instructions","date":"2024-01-29","arxiv_id":"2401.16468","repositories_listed":1,"syntology":null},{"url":"/paper/sliced-wasserstein-with-random-path","slug":"sliced-wasserstein-with-random-path","title":"Sliced Wasserstein with Random-Path Projecting Directions","date":"2024-01-29","arxiv_id":"2401.15889","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/sliced-wasserstein-with-random-path#ran","syntology_url":"https://syntology.ai/paper/2401.15889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15889"}},"official":{"repos":["khainb/rpsw"],"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/diffusion-based-graph-generative-methods","slug":"diffusion-based-graph-generative-methods","title":"Diffusion-based Graph Generative Methods","date":"2024-01-28","arxiv_id":"2401.15617","repositories_listed":1,"syntology":null},{"url":"/paper/cascadedgaze-efficiency-in-global-context","slug":"cascadedgaze-efficiency-in-global-context","title":"CascadedGaze: Efficiency in Global Context Extraction for Image Restoration","date":"2024-01-26","arxiv_id":"2401.15235","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"10 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cascadedgaze-efficiency-in-global-context#ran","syntology_url":"https://syntology.ai/paper/2401.15235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15235"}},"official":{"repos":["Ascend-Research/CascadedGaze"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-pre-trained-model-enables-universal","slug":"masked-pre-trained-model-enables-universal","title":"Masked Pre-training Enables Universal Zero-shot Denoiser","date":"2024-01-26","arxiv_id":"2401.14966","repositories_listed":1,"syntology":null},{"url":"/paper/deconstructing-denoising-diffusion-models-for","slug":"deconstructing-denoising-diffusion-models-for","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","date":"2024-01-25","arxiv_id":"2401.14404","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":4,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deconstructing-denoising-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2401.14404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14404"}},"official":null}},{"url":"/paper/progressive-multi-task-anti-noise-learning","slug":"progressive-multi-task-anti-noise-learning","title":"Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition","date":"2024-01-25","arxiv_id":"2401.14336","repositories_listed":1,"syntology":null},{"url":"/paper/consistency-guided-knowledge-retrieval-and","slug":"consistency-guided-knowledge-retrieval-and","title":"Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction","date":"2024-01-24","arxiv_id":"2401.13598","repositories_listed":1,"syntology":null},{"url":"/paper/denosent-a-denoising-objective-for-self","slug":"denosent-a-denoising-objective-for-self","title":"DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning","date":"2024-01-24","arxiv_id":"2401.13621","repositories_listed":1,"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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/denosent-a-denoising-objective-for-self#ran","syntology_url":"https://syntology.ai/paper/2401.13621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.13621"}},"official":{"repos":["xinghaow99/denosent"],"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/inverse-molecular-design-with-multi","slug":"inverse-molecular-design-with-multi","title":"Graph Diffusion Transformers for Multi-Conditional Molecular Generation","date":"2024-01-24","arxiv_id":"2401.13858","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/inverse-molecular-design-with-multi#ran","syntology_url":"https://syntology.ai/paper/2401.13858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.13858"}},"official":{"repos":["liugangcode/MCD"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dual-domain-coarse-to-fine-progressive","slug":"dual-domain-coarse-to-fine-progressive","title":"Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT","date":"2024-01-23","arxiv_id":"2401.13140","repositories_listed":1,"syntology":null},{"url":"/paper/lightdic-a-simple-yet-effective-approach-for","slug":"lightdic-a-simple-yet-effective-approach-for","title":"LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning","date":"2024-01-22","arxiv_id":"2401.11772","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"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) · 3 unverified","sample_list":"/paper/lightdic-a-simple-yet-effective-approach-for#ran","syntology_url":"https://syntology.ai/paper/2401.11772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11772"}},"official":{"repos":["xkli-allen/lightdic"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-diffusion-time-steps-for","slug":"exploring-diffusion-time-steps-for","title":"Exploring Diffusion Time-steps for Unsupervised Representation Learning","date":"2024-01-21","arxiv_id":"2401.11430","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-diffusion-time-steps-for#ran","syntology_url":"https://syntology.ai/paper/2401.11430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11430"}},"official":{"repos":["yue-zhongqi/diti"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/motionmix-weakly-supervised-diffusion-for","slug":"motionmix-weakly-supervised-diffusion-for","title":"MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation","date":"2024-01-20","arxiv_id":"2401.11115","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-are-efficient-learners","slug":"large-language-models-are-efficient-learners","title":"Large Language Models are Efficient Learners of Noise-Robust Speech Recognition","date":"2024-01-19","arxiv_id":"2401.10446","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":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/large-language-models-are-efficient-learners#ran","syntology_url":"https://syntology.ai/paper/2401.10446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10446"}},"official":{"repos":["yuchen005/robustger"],"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/automatic-tuning-of-denoising-algorithms","slug":"automatic-tuning-of-denoising-algorithms","title":"Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth","date":"2024-01-18","arxiv_id":"2401.09817","repositories_listed":1,"syntology":null},{"url":"/paper/fixed-point-diffusion-models","slug":"fixed-point-diffusion-models","title":"Fixed Point Diffusion Models","date":"2024-01-16","arxiv_id":"2401.08741","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":3,"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/fixed-point-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2401.08741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08741"}},"official":{"repos":["lukemelas/fixed-point-diffusion-models"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/kadel-knowledge-aware-denoising-learning-for","slug":"kadel-knowledge-aware-denoising-learning-for","title":"KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation","date":"2024-01-16","arxiv_id":"2401.08376","repositories_listed":1,"syntology":null},{"url":"/paper/registration-of-algebraic-varieties-using","slug":"registration-of-algebraic-varieties-using","title":"Registration of algebraic varieties using Riemannian optimization","date":"2024-01-16","arxiv_id":"2401.08562","repositories_listed":1,"syntology":null},{"url":"/paper/robust-tiny-object-detection-in-aerial-images","slug":"robust-tiny-object-detection-in-aerial-images","title":"Robust Tiny Object Detection in Aerial Images amidst Label Noise","date":"2024-01-16","arxiv_id":"2401.08056","repositories_listed":1,"syntology":null},{"url":"/paper/rohm-robust-human-motion-reconstruction-via","slug":"rohm-robust-human-motion-reconstruction-via","title":"RoHM: Robust Human Motion Reconstruction via Diffusion","date":"2024-01-16","arxiv_id":"2401.08570","repositories_listed":1,"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/rohm-robust-human-motion-reconstruction-via#ran","syntology_url":"https://syntology.ai/paper/2401.08570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08570"}},"official":null}},{"url":"/paper/denoising-diffusion-recommender-model","slug":"denoising-diffusion-recommender-model","title":"Denoising Diffusion Recommender Model","date":"2024-01-13","arxiv_id":"2401.06982","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"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) · 0 unverified","sample_list":"/paper/denoising-diffusion-recommender-model#ran","syntology_url":"https://syntology.ai/paper/2401.06982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06982"}},"official":{"repos":["polaris-jz/ddrm"],"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"]}}},{"url":"/paper/quantum-denoising-diffusion-models","slug":"quantum-denoising-diffusion-models","title":"Quantum Denoising Diffusion Models","date":"2024-01-13","arxiv_id":"2401.07049","repositories_listed":1,"syntology":null},{"url":"/paper/motion2vecsets-4d-latent-vector-set-diffusion","slug":"motion2vecsets-4d-latent-vector-set-diffusion","title":"Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking","date":"2024-01-12","arxiv_id":"2401.06614","repositories_listed":1,"syntology":null},{"url":"/paper/diffda-a-diffusion-model-for-weather-scale","slug":"diffda-a-diffusion-model-for-weather-scale","title":"DiffDA: a Diffusion Model for Weather-scale Data Assimilation","date":"2024-01-11","arxiv_id":"2401.05932","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/diffda-a-diffusion-model-for-weather-scale#ran","syntology_url":"https://syntology.ai/paper/2401.05932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05932"}},"official":{"repos":["spcl/diffda"],"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/erasediff-erasing-data-influence-in-diffusion","slug":"erasediff-erasing-data-influence-in-diffusion","title":"Erasing Undesirable Influence in Diffusion Models","date":"2024-01-11","arxiv_id":"2401.05779","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"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) · 1 unverified","sample_list":"/paper/erasediff-erasing-data-influence-in-diffusion#ran","syntology_url":"https://syntology.ai/paper/2401.05779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05779"}},"official":{"repos":["jingwu321/erasediff"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/fedtabdiff-federated-learning-of-diffusion","slug":"fedtabdiff-federated-learning-of-diffusion","title":"FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation","date":"2024-01-11","arxiv_id":"2401.06263","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-pose-refinement-and-muti","slug":"diffusion-based-pose-refinement-and-muti","title":"Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose Estimaiton","date":"2024-01-10","arxiv_id":"2401.04921","repositories_listed":1,"syntology":null},{"url":"/paper/d3ad-dynamic-denoising-diffusion","slug":"d3ad-dynamic-denoising-diffusion","title":"Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection","date":"2024-01-09","arxiv_id":"2401.04463","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-distribution-alignment-for-post","slug":"enhanced-distribution-alignment-for-post","title":"EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models","date":"2024-01-09","arxiv_id":"2401.04585","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhanced-distribution-alignment-for-post#ran","syntology_url":"https://syntology.ai/paper/2401.04585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04585"}},"official":{"repos":["BienLuky/EDA-DM"],"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/robust-image-watermarking-using-stable","slug":"robust-image-watermarking-using-stable","title":"Attack-Resilient Image Watermarking Using Stable Diffusion","date":"2024-01-08","arxiv_id":"2401.04247","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":11,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/robust-image-watermarking-using-stable#ran","syntology_url":"https://syntology.ai/paper/2401.04247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04247"}},"official":{"repos":["zhanglijun95/ZoDiac"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/denoising-vision-transformers","slug":"denoising-vision-transformers","title":"Denoising Vision Transformers","date":"2024-01-05","arxiv_id":"2401.02957","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/denoising-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2401.02957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02957"}},"official":{"repos":["Jiawei-Yang/Denoising-ViT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-variational-inference-diffusion","slug":"diffusion-variational-inference-diffusion","title":"Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors","date":"2024-01-05","arxiv_id":"2401.02739","repositories_listed":1,"syntology":null},{"url":"/paper/geometric-facilitated-denoising-diffusion","slug":"geometric-facilitated-denoising-diffusion","title":"Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation","date":"2024-01-05","arxiv_id":"2401.02683","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":0,"phrase":"13 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geometric-facilitated-denoising-diffusion#ran","syntology_url":"https://syntology.ai/paper/2401.02683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02683"}},"official":{"repos":["LEOXC1571/GFMDiff"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/two-stage-progressive-residual-dense","slug":"two-stage-progressive-residual-dense","title":"Two-stage Progressive Residual Dense Attention Network for Image Denoising","date":"2024-01-05","arxiv_id":"2401.02831","repositories_listed":1,"syntology":null},{"url":"/paper/diffusionedge-diffusion-probabilistic-model","slug":"diffusionedge-diffusion-probabilistic-model","title":"DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection","date":"2024-01-04","arxiv_id":"2401.02032","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":2,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 2 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffusionedge-diffusion-probabilistic-model#ran","syntology_url":"https://syntology.ai/paper/2401.02032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02032"}},"official":{"repos":["guhuangai/diffusionedge"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/boosting-of-implicit-neural-representation","slug":"boosting-of-implicit-neural-representation","title":"Boosting of Implicit Neural Representation-based Image Denoiser","date":"2024-01-03","arxiv_id":"2401.01548","repositories_listed":1,"syntology":null},{"url":"/paper/deil-direct-and-inverse-clip-for-open-world","slug":"deil-direct-and-inverse-clip-for-open-world","title":"DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/denoising-point-clouds-in-latent-space-via","slug":"denoising-point-clouds-in-latent-space-via","title":"Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural Network","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/diffloc-diffusion-model-for-outdoor-lidar","slug":"diffloc-diffusion-model-for-outdoor-lidar","title":"DiffLoc: Diffusion Model for Outdoor LiDAR Localization","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dual-prior-unfolding-for-snapshot-compressive","slug":"dual-prior-unfolding-for-snapshot-compressive","title":"Dual Prior Unfolding for Snapshot Compressive Imaging","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/flowdiffuser-advancing-optical-flow","slug":"flowdiffuser-advancing-optical-flow","title":"FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inversion-free-image-editing-with-language","slug":"inversion-free-image-editing-with-language","title":"Inversion-Free Image Editing with Language-Guided Diffusion Models","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multiway-point-cloud-mosaicking-with","slug":"multiway-point-cloud-mosaicking-with","title":"Multiway Point Cloud Mosaicking with Diffusion and Global Optimization","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/real-world-mobile-image-denoising-dataset","slug":"real-world-mobile-image-denoising-dataset","title":"Real-World Mobile Image Denoising Dataset with Efficient Baselines","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-image-denoising-through-adversarial","slug":"robust-image-denoising-through-adversarial","title":"Robust Image Denoising through Adversarial Frequency Mixup","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/z-zero-shot-style-transfer-via-attention-1","slug":"z-zero-shot-style-transfer-via-attention-1","title":"Z*: Zero-shot Style Transfer via Attention Reweighting","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/zero-ig-zero-shot-illumination-guided-joint","slug":"zero-ig-zero-shot-illumination-guided-joint","title":"ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light Images","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-image-denoising-via-spatial","slug":"hyperspectral-image-denoising-via-spatial","title":"Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer","date":"2023-12-31","arxiv_id":"2401.03885","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-supervised-graph","slug":"data-augmentation-for-supervised-graph","title":"Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models","date":"2023-12-29","arxiv_id":"2312.17679","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/data-augmentation-for-supervised-graph#ran","syntology_url":"https://syntology.ai/paper/2312.17679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17679"}},"official":{"repos":["kayzliu/godm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-image-restoration-through-removing","slug":"improving-image-restoration-through-removing","title":"Improving Image Restoration through Removing Degradations in Textual Representations","date":"2023-12-28","arxiv_id":"2312.17334","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-dynamic-correlations-and","slug":"learning-the-dynamic-correlations-and","title":"Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting","date":"2023-12-28","arxiv_id":"2312.16790","repositories_listed":1,"syntology":null},{"url":"/paper/image-restoration-by-denoising-diffusion","slug":"image-restoration-by-denoising-diffusion","title":"Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance","date":"2023-12-27","arxiv_id":"2312.16519","repositories_listed":1,"syntology":{"n":22,"n_ran":20,"n_constructed":0,"n_ran_checked":12,"n_instrument":8,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":22,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/image-restoration-by-denoising-diffusion#ran","syntology_url":"https://syntology.ai/paper/2312.16519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16519"}},"official":{"repos":["tirer-lab/ddpg"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/plug-and-play-regularization-on-magnitude","slug":"plug-and-play-regularization-on-magnitude","title":"Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging","date":"2023-12-26","arxiv_id":"2312.16024","repositories_listed":1,"syntology":null},{"url":"/paper/toward-accurate-and-temporally-consistent","slug":"toward-accurate-and-temporally-consistent","title":"Toward Accurate and Temporally Consistent Video Restoration from Raw Data","date":"2023-12-25","arxiv_id":"2312.16247","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-the-style-content-trade-off-in-1","slug":"balancing-the-style-content-trade-off-in-1","title":"Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising","date":"2023-12-22","arxiv_id":"2312.14708","repositories_listed":1,"syntology":null},{"url":"/paper/dreaming-of-electrical-waves-generative","slug":"dreaming-of-electrical-waves-generative","title":"Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models","date":"2023-12-22","arxiv_id":"2312.14830","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-forecasting-models-via-gaussian","slug":"fine-grained-forecasting-models-via-gaussian","title":"Fine-grained Forecasting Models Via Gaussian Process Blurring Effect","date":"2023-12-21","arxiv_id":"2312.14280","repositories_listed":1,"syntology":null},{"url":"/paper/diffportrait3d-controllable-diffusion-for","slug":"diffportrait3d-controllable-diffusion-for","title":"DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis","date":"2023-12-20","arxiv_id":"2312.13016","repositories_listed":1,"syntology":null},{"url":"/paper/dvis-improved-decoupled-framework-for","slug":"dvis-improved-decoupled-framework-for","title":"DVIS++: Improved Decoupled Framework for Universal Video Segmentation","date":"2023-12-20","arxiv_id":"2312.13305","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-guidance-training-free-acceleration","slug":"adaptive-guidance-training-free-acceleration","title":"Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models","date":"2023-12-19","arxiv_id":"2312.12487","repositories_listed":1,"syntology":null},{"url":"/paper/fontdiffuser-one-shot-font-generation-via","slug":"fontdiffuser-one-shot-font-generation-via","title":"FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning","date":"2023-12-19","arxiv_id":"2312.12142","repositories_listed":1,"syntology":null},{"url":"/paper/on-inference-stability-for-diffusion-models","slug":"on-inference-stability-for-diffusion-models","title":"On Inference Stability for Diffusion Models","date":"2023-12-19","arxiv_id":"2312.12431","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":14,"phrase":"11 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-inference-stability-for-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2312.12431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12431"}},"official":{"repos":["vinairesearch/sa-dpm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/segrefiner-towards-model-agnostic-1","slug":"segrefiner-towards-model-agnostic-1","title":"SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process","date":"2023-12-19","arxiv_id":"2312.12425","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":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/segrefiner-towards-model-agnostic-1#ran","syntology_url":"https://syntology.ai/paper/2312.12425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12425"}},"official":{"repos":["mengyuwang826/segrefiner"],"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/streamdiffusion-a-pipeline-level-solution-for","slug":"streamdiffusion-a-pipeline-level-solution-for","title":"StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation","date":"2023-12-19","arxiv_id":"2312.12491","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":9,"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/streamdiffusion-a-pipeline-level-solution-for#ran","syntology_url":"https://syntology.ai/paper/2312.12491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12491"}},"official":{"repos":["cumulo-autumn/streamdiffusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-a-diffusion-model-policy-from","slug":"learning-a-diffusion-model-policy-from","title":"Learning a Diffusion Model Policy from Rewards via Q-Score Matching","date":"2023-12-18","arxiv_id":"2312.11752","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/learning-a-diffusion-model-policy-from#ran","syntology_url":"https://syntology.ai/paper/2312.11752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11752"}},"official":{"repos":["Alescontrela/score_matching_rl"],"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":["official"]}}},{"url":"/paper/dreamtalk-when-expressive-talking-head","slug":"dreamtalk-when-expressive-talking-head","title":"DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models","date":"2023-12-15","arxiv_id":"2312.09767","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/dreamtalk-when-expressive-talking-head#ran","syntology_url":"https://syntology.ai/paper/2312.09767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09767"}},"official":{"repos":["ali-vilab/dreamtalk"],"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/faster-diffusion-rethinking-the-role-of-unet","slug":"faster-diffusion-rethinking-the-role-of-unet","title":"Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference","date":"2023-12-15","arxiv_id":"2312.09608","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/faster-diffusion-rethinking-the-role-of-unet#ran","syntology_url":"https://syntology.ai/paper/2312.09608","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09608"}},"official":{"repos":["hutaihang/faster-diffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/focus-on-your-instruction-fine-grained-and","slug":"focus-on-your-instruction-fine-grained-and","title":"Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation","date":"2023-12-15","arxiv_id":"2312.10113","repositories_listed":1,"syntology":null},{"url":"/paper/nm-flowgan-modeling-srgb-noise-with-a-hybrid","slug":"nm-flowgan-modeling-srgb-noise-with-a-hybrid","title":"NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN","date":"2023-12-15","arxiv_id":"2312.10112","repositories_listed":1,"syntology":null},{"url":"/paper/ppfm-image-denoising-in-photon-counting-ct","slug":"ppfm-image-denoising-in-photon-counting-ct","title":"PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models","date":"2023-12-15","arxiv_id":"2312.09754","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-scalable-graph-generation","slug":"efficient-and-scalable-graph-generation","title":"Efficient and Scalable Graph Generation through Iterative Local Expansion","date":"2023-12-14","arxiv_id":"2312.11529","repositories_listed":1,"syntology":null},{"url":"/paper/latenteditor-text-driven-local-editing-of-3d","slug":"latenteditor-text-driven-local-editing-of-3d","title":"LatentEditor: Text Driven Local Editing of 3D Scenes","date":"2023-12-14","arxiv_id":"2312.09313","repositories_listed":1,"syntology":null},{"url":"/paper/clockwork-diffusion-efficient-generation-with","slug":"clockwork-diffusion-efficient-generation-with","title":"Clockwork Diffusion: Efficient Generation With Model-Step Distillation","date":"2023-12-13","arxiv_id":"2312.08128","repositories_listed":1,"syntology":null},{"url":"/paper/erase-error-resilient-representation-learning","slug":"erase-error-resilient-representation-learning","title":"ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance","date":"2023-12-13","arxiv_id":"2312.08852","repositories_listed":1,"syntology":null},{"url":"/paper/lmd-faster-image-reconstruction-with-latent","slug":"lmd-faster-image-reconstruction-with-latent","title":"LMD: Faster Image Reconstruction with Latent Masking Diffusion","date":"2023-12-13","arxiv_id":"2312.07971","repositories_listed":1,"syntology":null},{"url":"/paper/r-diffusion-a-diffusion-based-density","slug":"r-diffusion-a-diffusion-based-density","title":"$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics","date":"2023-12-13","arxiv_id":"2312.08153","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/r-diffusion-a-diffusion-based-density#ran","syntology_url":"https://syntology.ai/paper/2312.08153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08153"}},"official":{"repos":["intel/rho-diffusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-driven-initial-image-construction","slug":"semantic-driven-initial-image-construction","title":"The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization","date":"2023-12-13","arxiv_id":"2312.08872","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":1,"n_honours":2,"n_violates":3,"n_no_contract":2,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 3 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-driven-initial-image-construction#ran","syntology_url":"https://syntology.ai/paper/2312.08872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08872"}},"official":{"repos":["UT-Mao/Initial-Noise-Construction"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/simac-a-simple-anti-customization-method","slug":"simac-a-simple-anti-customization-method","title":"SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models","date":"2023-12-13","arxiv_id":"2312.07865","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"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) · 4 unverified","sample_list":"/paper/simac-a-simple-anti-customization-method#ran","syntology_url":"https://syntology.ai/paper/2312.07865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07865"}},"official":{"repos":["somuchtome/simac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/spd-ddpm-denoising-diffusion-probabilistic","slug":"spd-ddpm-denoising-diffusion-probabilistic","title":"SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space","date":"2023-12-13","arxiv_id":"2312.08200","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":13,"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) · 1 unverified","sample_list":"/paper/spd-ddpm-denoising-diffusion-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2312.08200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08200"}},"official":{"repos":["li-yun-chen/spd-ddpm"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/world-models-via-policy-guided-trajectory","slug":"world-models-via-policy-guided-trajectory","title":"World Models via Policy-Guided Trajectory Diffusion","date":"2023-12-13","arxiv_id":"2312.08533","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":2,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 2 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/world-models-via-policy-guided-trajectory#ran","syntology_url":"https://syntology.ai/paper/2312.08533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08533"}},"official":{"repos":["marc-rigter/polygrad-world-models"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptir-parameter-efficient-multi-task","slug":"adaptir-parameter-efficient-multi-task","title":"Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts","date":"2023-12-12","arxiv_id":"2312.08881","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":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/adaptir-parameter-efficient-multi-task#ran","syntology_url":"https://syntology.ai/paper/2312.08881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08881"}},"official":{"repos":["csguoh/adaptir"],"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/freeinit-bridging-initialization-gap-in-video","slug":"freeinit-bridging-initialization-gap-in-video","title":"FreeInit: Bridging Initialization Gap in Video Diffusion Models","date":"2023-12-12","arxiv_id":"2312.07537","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":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/freeinit-bridging-initialization-gap-in-video#ran","syntology_url":"https://syntology.ai/paper/2312.07537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07537"}},"official":{"repos":["tianxingwu/freeinit"],"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/characteristic-guidance-non-linear-correction","slug":"characteristic-guidance-non-linear-correction","title":"Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale","date":"2023-12-11","arxiv_id":"2312.07586","repositories_listed":1,"syntology":null},{"url":"/paper/compensation-sampling-for-improved","slug":"compensation-sampling-for-improved","title":"Compensation Sampling for Improved Convergence in Diffusion Models","date":"2023-12-11","arxiv_id":"2312.06285","repositories_listed":1,"syntology":null},{"url":"/paper/csot-curriculum-and-structure-aware-optimal-1","slug":"csot-curriculum-and-structure-aware-optimal-1","title":"CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels","date":"2023-12-11","arxiv_id":"2312.06221","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"6 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; 5 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/csot-curriculum-and-structure-aware-optimal-1#ran","syntology_url":"https://syntology.ai/paper/2312.06221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06221"}},"official":{"repos":["changwxx/csot-for-lnl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/diad-a-diffusion-based-framework-for-multi","slug":"diad-a-diffusion-based-framework-for-multi","title":"DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection","date":"2023-12-11","arxiv_id":"2312.06607","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":3,"phrase":"9 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diad-a-diffusion-based-framework-for-multi#ran","syntology_url":"https://syntology.ai/paper/2312.06607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06607"}},"official":{"repos":["lewandofskee/DiAD"],"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/malpurifier-enhancing-android-malware","slug":"malpurifier-enhancing-android-malware","title":"MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks","date":"2023-12-11","arxiv_id":"2312.06423","repositories_listed":1,"syntology":null},{"url":"/paper/optimized-view-and-geometry-distillation-from","slug":"optimized-view-and-geometry-distillation-from","title":"Optimized View and Geometry Distillation from Multi-view Diffuser","date":"2023-12-11","arxiv_id":"2312.06198","repositories_listed":1,"syntology":null},{"url":"/paper/robust-graph-neural-network-based-on-graph","slug":"robust-graph-neural-network-based-on-graph","title":"Robust Graph Neural Network based on Graph Denoising","date":"2023-12-11","arxiv_id":"2312.06557","repositories_listed":1,"syntology":null},{"url":"/paper/textual-prompt-guided-image-restoration","slug":"textual-prompt-guided-image-restoration","title":"Textual Prompt Guided Image Restoration","date":"2023-12-11","arxiv_id":"2312.06162","repositories_listed":1,"syntology":null},{"url":"/paper/the-journey-not-the-destination-how-data","slug":"the-journey-not-the-destination-how-data","title":"The Journey, Not the Destination: How Data Guides Diffusion Models","date":"2023-12-11","arxiv_id":"2312.06205","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":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/the-journey-not-the-destination-how-data#ran","syntology_url":"https://syntology.ai/paper/2312.06205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06205"}},"official":{"repos":["madrylab/journey-trak"],"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/visiontraj-a-noise-robust-trajectory-recovery","slug":"visiontraj-a-noise-robust-trajectory-recovery","title":"VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network","date":"2023-12-11","arxiv_id":"2312.06428","repositories_listed":1,"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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/visiontraj-a-noise-robust-trajectory-recovery#ran","syntology_url":"https://syntology.ai/paper/2312.06428","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06428"}},"official":{"repos":["bonaldli/visiontraj"],"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/consistency-models-for-scalable-and-fast","slug":"consistency-models-for-scalable-and-fast","title":"Consistency Models for Scalable and Fast Simulation-Based Inference","date":"2023-12-09","arxiv_id":"2312.05440","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":5,"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/consistency-models-for-scalable-and-fast#ran","syntology_url":"https://syntology.ai/paper/2312.05440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05440"}},"official":{"repos":["bayesflow-org/consistency-model-posterior-estimation"],"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/d3a-ts-denoising-driven-data-augmentation-in","slug":"d3a-ts-denoising-driven-data-augmentation-in","title":"D3A-TS: Denoising-Driven Data Augmentation in Time Series","date":"2023-12-09","arxiv_id":"2312.05550","repositories_listed":1,"syntology":null},{"url":"/paper/dposer-diffusion-model-as-robust-3d-human","slug":"dposer-diffusion-model-as-robust-3d-human","title":"DPoser: Diffusion Model as Robust 3D Human Pose Prior","date":"2023-12-09","arxiv_id":"2312.05541","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-denoise-unreliable-interactions","slug":"learning-to-denoise-unreliable-interactions","title":"Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction","date":"2023-12-09","arxiv_id":"2312.06682","repositories_listed":1,"syntology":null},{"url":"/paper/diffcmr-fast-cardiac-mri-reconstruction-with","slug":"diffcmr-fast-cardiac-mri-reconstruction-with","title":"DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models","date":"2023-12-08","arxiv_id":"2312.04853","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-in-prompt-learning-for-universal-image","slug":"prompt-in-prompt-learning-for-universal-image","title":"Prompt-In-Prompt Learning for Universal Image Restoration","date":"2023-12-08","arxiv_id":"2312.05038","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/prompt-in-prompt-learning-for-universal-image#ran","syntology_url":"https://syntology.ai/paper/2312.05038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05038"}},"official":{"repos":["longzilicart/pip_universal"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-aware-surrogate-models-for","slug":"uncertainty-aware-surrogate-models-for","title":"Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models","date":"2023-12-08","arxiv_id":"2312.05320","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":11,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 2 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uncertainty-aware-surrogate-models-for#ran","syntology_url":"https://syntology.ai/paper/2312.05320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05320"}},"official":{"repos":["tum-pbs/diffusion-based-flow-prediction"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"5bfe9e7ebb58d4ca945ce54d5eb2a5214abfc5cca9fa233fa8c683a3eb79bda6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}