{"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":"/method/diffusion/papers/110","list_of":"/method/diffusion","method":"Diffusion","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":110,"pages_in_order":139,"rows_per_page":100,"rows":[10901,11000],"of":13848,"counts":{"archive_papers_tagged":13848,"with_a_code_link":5365,"where_syntology_ran_a_sample":2249,"not_listed_spam_title":0,"listed":13848,"listed_where_code_ran":2249,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1969,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1969,"listed_every_run_a_failure_of_syntologys_instrument":280,"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":"/method/diffusion","prev":"/method/diffusion/papers/109","next":"/method/diffusion/papers/111","papers":[{"paper":"/paper/polyffusion-a-diffusion-model-for-polyphonic","slug":"polyffusion-a-diffusion-model-for-polyphonic","title":"Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls","date":"2023-07-19","arxiv_id":"2307.10304","n_code_links":1,"syntology":null},{"paper":"/paper/prediff-precipitation-nowcasting-with-latent-1","slug":"prediff-precipitation-nowcasting-with-latent-1","title":"PreDiff: Precipitation Nowcasting with Latent Diffusion Models","date":"2023-07-19","arxiv_id":"2307.10422","n_code_links":1,"syntology":{"ran":9,"of":12,"n_ran_checked":6,"n_instrument":3,"unverified":3,"pointer_only":3,"phrase":"9 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; 3 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"text2layer-layered-image-generation-using","title":"Text2Layer: Layered Image Generation using Latent Diffusion Model","date":"2023-07-19","arxiv_id":"2307.09781","n_code_links":0,"syntology":null},{"paper":"/paper/tokenflow-consistent-diffusion-features-for","slug":"tokenflow-consistent-diffusion-features-for","title":"TokenFlow: Consistent Diffusion Features for Consistent Video Editing","date":"2023-07-19","arxiv_id":"2307.10373","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":4,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/xskill-cross-embodiment-skill-discovery","slug":"xskill-cross-embodiment-skill-discovery","title":"XSkill: Cross Embodiment Skill Discovery","date":"2023-07-19","arxiv_id":"2307.09955","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":0,"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","official":{"repos":["real-stanford/xskill"],"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"]}}},{"paper":null,"slug":"ditto-diffusion-inspired-temporal-transformer","title":"Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)","date":"2023-07-18","arxiv_id":"2307.09072","n_code_links":0,"syntology":null},{"paper":"/paper/dreamr-diffusion-driven-counterfactual","slug":"dreamr-diffusion-driven-counterfactual","title":"DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI","date":"2023-07-18","arxiv_id":"2307.09547","n_code_links":1,"syntology":null},{"paper":"/paper/towards-authentic-face-restoration-with","slug":"towards-authentic-face-restoration-with","title":"Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond","date":"2023-07-18","arxiv_id":"2307.08996","n_code_links":0,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"an-indefensible-attack-label-only-model","title":"Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model","date":"2023-07-17","arxiv_id":"2307.08424","n_code_links":0,"syntology":null},{"paper":"/paper/autoregressive-diffusion-model-for-graph","slug":"autoregressive-diffusion-model-for-graph","title":"Autoregressive Diffusion Model for Graph Generation","date":"2023-07-17","arxiv_id":"2307.08849","n_code_links":1,"syntology":null},{"paper":null,"slug":"complexity-matters-rethinking-the-latent","title":"Complexity Matters: Rethinking the Latent Space for Generative Modeling","date":"2023-07-17","arxiv_id":"2307.08283","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-models-beat-gans-on-image","slug":"diffusion-models-beat-gans-on-image","title":"Diffusion Models Beat GANs on Image Classification","date":"2023-07-17","arxiv_id":"2307.08702","n_code_links":1,"syntology":null},{"paper":"/paper/identity-preserving-aging-of-face-images-via","slug":"identity-preserving-aging-of-face-images-via","title":"Identity-Preserving Aging of Face Images via Latent Diffusion Models","date":"2023-07-17","arxiv_id":"2307.08585","n_code_links":1,"syntology":{"ran":14,"of":16,"n_ran_checked":9,"n_instrument":5,"unverified":2,"pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 3 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","official":{"repos":["sudban3089/ID-Preserving-Facial-Aging"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"manifold-guided-sampling-in-diffusion-models","title":"Unbiased Image Synthesis via Manifold Guidance in Diffusion Models","date":"2023-07-17","arxiv_id":"2307.08199","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-diffusion-segmentation-model-for","title":"Multimodal Diffusion Segmentation Model for Object Segmentation from Manipulation Instructions","date":"2023-07-17","arxiv_id":"2307.08597","n_code_links":0,"syntology":null},{"paper":"/paper/not-all-steps-are-created-equal-selective","slug":"not-all-steps-are-created-equal-selective","title":"Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation","date":"2023-07-17","arxiv_id":"2307.08448","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":1,"n_instrument":6,"unverified":0,"pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["andysonys/selective-diffusion-distillation"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"semi-diffusioninst-a-diffusion-model-based","title":"SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation","date":"2023-07-17","arxiv_id":"2307.08693","n_code_links":0,"syntology":null},{"paper":"/paper/synthetic-lagrangian-turbulence-by-generative","slug":"synthetic-lagrangian-turbulence-by-generative","title":"Synthetic Lagrangian Turbulence by Generative Diffusion Models","date":"2023-07-17","arxiv_id":"2307.08529","n_code_links":1,"syntology":{"ran":14,"of":20,"n_ran_checked":9,"n_instrument":5,"unverified":6,"pointer_only":12,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 6 unverified","official":{"repos":["smartturb/diffusion-lagr"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/analysing-gender-bias-in-text-to-image-models","slug":"analysing-gender-bias-in-text-to-image-models","title":"Analysing Gender Bias in Text-to-Image Models using Object Detection","date":"2023-07-16","arxiv_id":"2307.08025","n_code_links":1,"syntology":null},{"paper":null,"slug":"diffusion-to-confusion-naturalistic","title":"Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector","date":"2023-07-16","arxiv_id":"2307.08076","n_code_links":0,"syntology":null},{"paper":null,"slug":"lafite-latent-diffusion-model-with-feature","title":"LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection","date":"2023-07-16","arxiv_id":"2307.08059","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-orientation-distribution-fields-for","title":"Neural Orientation Distribution Fields for Estimation and Uncertainty Quantification in Diffusion MRI","date":"2023-07-16","arxiv_id":"2307.08138","n_code_links":0,"syntology":null},{"paper":"/paper/noise-aware-speech-enhancement-using","slug":"noise-aware-speech-enhancement-using","title":"Noise-aware Speech Enhancement using Diffusion Probabilistic Model","date":"2023-07-16","arxiv_id":"2307.08029","n_code_links":1,"syntology":null},{"paper":"/paper/planting-a-seed-of-vision-in-large-language","slug":"planting-a-seed-of-vision-in-large-language","title":"Planting a SEED of Vision in Large Language Model","date":"2023-07-16","arxiv_id":"2307.08041","n_code_links":1,"syntology":null},{"paper":"/paper/solving-inverse-problems-with-latent","slug":"solving-inverse-problems-with-latent","title":"Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency","date":"2023-07-16","arxiv_id":"2307.08123","n_code_links":1,"syntology":null},{"paper":null,"slug":"adjusting-the-nuclear-reactor-s-neutron","title":"Adjusting the nuclear reactor's neutron transport and diffusion theory for an alternative description and modelling of postage or supplies delivery processes","date":"2023-07-15","arxiv_id":"2307.07867","n_code_links":0,"syntology":null},{"paper":"/paper/exposurediffusion-learning-to-expose-for-low","slug":"exposurediffusion-learning-to-expose-for-low","title":"ExposureDiffusion: Learning to Expose for Low-light Image Enhancement","date":"2023-07-15","arxiv_id":"2307.07710","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-adaptation-with-bradley-terry-preference","title":"Fast Adaptation with Bradley-Terry Preference Models in Text-To-Image Classification and Generation","date":"2023-07-15","arxiv_id":"2308.07929","n_code_links":0,"syntology":null},{"paper":null,"slug":"dreamteacher-pretraining-image-backbones-with","title":"DreamTeacher: Pretraining Image Backbones with Deep Generative Models","date":"2023-07-14","arxiv_id":"2307.07487","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-learning-empowered-ai-generated","title":"Federated Learning-Empowered AI-Generated Content in Wireless Networks","date":"2023-07-14","arxiv_id":"2307.07146","n_code_links":0,"syntology":null},{"paper":null,"slug":"inverse-evolution-layers-physics-informed","title":"Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks","date":"2023-07-14","arxiv_id":"2307.07344","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-motion-conditioned-diffusion-model","slug":"multimodal-motion-conditioned-diffusion-model","title":"Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection","date":"2023-07-14","arxiv_id":"2307.07205","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["aleflabo/MoCoDAD"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"nifty-neural-object-interaction-fields-for","title":"NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis","date":"2023-07-14","arxiv_id":"2307.07511","n_code_links":0,"syntology":null},{"paper":null,"slug":"avatarfusion-zero-shot-generation-of-clothing","title":"AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion","date":"2023-07-13","arxiv_id":"2307.06526","n_code_links":0,"syntology":null},{"paper":"/paper/hyperdreambooth-hypernetworks-for-fast","slug":"hyperdreambooth-hypernetworks-for-fast","title":"HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models","date":"2023-07-13","arxiv_id":"2307.06949","n_code_links":2,"syntology":{"ran":2,"of":7,"n_ran_checked":2,"n_instrument":0,"unverified":5,"pointer_only":1,"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) · 5 unverified","official":null}},{"paper":null,"slug":"improving-nonalcoholic-fatty-liver-disease","title":"Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models","date":"2023-07-13","arxiv_id":"2307.06507","n_code_links":0,"syntology":null},{"paper":null,"slug":"neuro-symbolic-empowered-denoising-diffusion","title":"Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0","date":"2023-07-13","arxiv_id":"2307.06975","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-contract-design-via-relaxation","title":"Optimal contract design via relaxation: application to the problem of brokerage fee for a client with private signal","date":"2023-07-13","arxiv_id":"2307.07010","n_code_links":0,"syntology":null},{"paper":null,"slug":"pc-droid-faster-diffusion-and-improved","title":"PC-Droid: Faster diffusion and improved quality for particle cloud generation","date":"2023-07-13","arxiv_id":"2307.06836","n_code_links":0,"syntology":null},{"paper":"/paper/reward-directed-conditional-diffusion","slug":"reward-directed-conditional-diffusion","title":"Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement","date":"2023-07-13","arxiv_id":"2307.07055","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":null}},{"paper":null,"slug":"rician-likelihood-loss-for-quantitative-mri","title":"Rician likelihood loss for quantitative MRI using self-supervised deep learning","date":"2023-07-13","arxiv_id":"2307.07072","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusegae-controllable-and-high-fidelity","title":"DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation","date":"2023-07-12","arxiv_id":"2307.05899","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-based-multi-agent-adversarial","slug":"diffusion-based-multi-agent-adversarial","title":"Diffusion Models for Multi-target Adversarial Tracking","date":"2023-07-12","arxiv_id":"2307.06244","n_code_links":1,"syntology":null},{"paper":null,"slug":"exposing-the-fake-effective-diffusion","title":"Exposing the Fake: Effective Diffusion-Generated Images Detection","date":"2023-07-12","arxiv_id":"2307.06272","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-stochastic-dynamical-systems-as-an","title":"Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks","date":"2023-07-12","arxiv_id":"2307.06097","n_code_links":0,"syntology":null},{"paper":"/paper/towards-safe-self-distillation-of-internet","slug":"towards-safe-self-distillation-of-internet","title":"Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models","date":"2023-07-12","arxiv_id":"2307.05977","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["nannullna/safe-diffusion"],"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"]}}},{"paper":null,"slug":"ddgm-solving-inverse-problems-by-diffusive","title":"DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization","date":"2023-07-11","arxiv_id":"2307.04946","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-idea-exploration-for-art-generation","title":"Diffusion idea exploration for art generation","date":"2023-07-11","arxiv_id":"2307.04978","n_code_links":0,"syntology":null},{"paper":null,"slug":"merging-multiple-input-descriptors-and","title":"Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering","date":"2023-07-11","arxiv_id":"2307.05786","n_code_links":0,"syntology":null},{"paper":"/paper/metropolis-sampling-for-constrained-diffusion","slug":"metropolis-sampling-for-constrained-diffusion","title":"Metropolis Sampling for Constrained Diffusion Models","date":"2023-07-11","arxiv_id":"2307.05439","n_code_links":0,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"on-the-vulnerability-of-deepfake-detectors-to","title":"On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models","date":"2023-07-11","arxiv_id":"2307.05397","n_code_links":0,"syntology":null},{"paper":"/paper/animatediff-animate-your-personalized-text-to","slug":"animatediff-animate-your-personalized-text-to","title":"AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning","date":"2023-07-10","arxiv_id":"2307.04725","n_code_links":8,"syntology":{"ran":6,"of":10,"n_ran_checked":5,"n_instrument":1,"unverified":4,"pointer_only":2,"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) · 4 unverified","official":{"repos":["guoyww/animatediff"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"articulated-3d-head-avatar-generation-using","title":"Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models","date":"2023-07-10","arxiv_id":"2307.04859","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-policies-for-out-of-distribution","title":"Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning","date":"2023-07-10","arxiv_id":"2307.04726","n_code_links":0,"syntology":null},{"paper":null,"slug":"divide-evaluate-and-refine-evaluating-and","title":"Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback","date":"2023-07-10","arxiv_id":"2307.04749","n_code_links":0,"syntology":null},{"paper":"/paper/exact-diffusion-inversion-via-bi-directional","slug":"exact-diffusion-inversion-via-bi-directional","title":"Exact Diffusion Inversion via Bi-directional Integration Approximation","date":"2023-07-10","arxiv_id":"2307.10829","n_code_links":1,"syntology":{"ran":10,"of":16,"n_ran_checked":8,"n_instrument":2,"unverified":6,"pointer_only":16,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","official":{"repos":["guoqiang-zhang-x/BDIA"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"geometric-constraints-in-probabilistic","title":"Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes","date":"2023-07-10","arxiv_id":"2307.04493","n_code_links":0,"syntology":null},{"paper":null,"slug":"timbre-transfer-using-image-to-image","title":"Timbre transfer using image-to-image denoising diffusion implicit models","date":"2023-07-10","arxiv_id":"2307.04586","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenters-at-semeval-2023-task-1-enhancing","title":"Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion","date":"2023-07-09","arxiv_id":"2307.05564","n_code_links":0,"syntology":null},{"paper":null,"slug":"diff-nst-diffusion-interleaving-for","title":"DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer","date":"2023-07-09","arxiv_id":"2307.04157","n_code_links":0,"syntology":null},{"paper":null,"slug":"seismic-data-interpolation-based-on-denoising","title":"Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling","date":"2023-07-09","arxiv_id":"2307.04226","n_code_links":0,"syntology":null},{"paper":null,"slug":"measuring-the-success-of-diffusion-models-at","title":"Measuring the Success of Diffusion Models at Imitating Human Artists","date":"2023-07-08","arxiv_id":"2307.04028","n_code_links":0,"syntology":null},{"paper":"/paper/stimulating-the-diffusion-model-for-image","slug":"stimulating-the-diffusion-model-for-image","title":"Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling","date":"2023-07-08","arxiv_id":"2307.03992","n_code_links":1,"syntology":null},{"paper":null,"slug":"tractgeonet-a-geometric-deep-learning","title":"TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance","date":"2023-07-08","arxiv_id":"2307.03982","n_code_links":0,"syntology":null},{"paper":"/paper/autodecoding-latent-3d-diffusion-models-1","slug":"autodecoding-latent-3d-diffusion-models-1","title":"AutoDecoding Latent 3D Diffusion Models","date":"2023-07-07","arxiv_id":"2307.05445","n_code_links":1,"syntology":null},{"paper":"/paper/hyperspectral-and-multispectral-image-fusion-2","slug":"hyperspectral-and-multispectral-image-fusion-2","title":"Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic Model","date":"2023-07-07","arxiv_id":"2307.03423","n_code_links":1,"syntology":null},{"paper":"/paper/simulation-free-schrodinger-bridges-via-score","slug":"simulation-free-schrodinger-bridges-via-score","title":"Simulation-free Schrödinger bridges via score and flow matching","date":"2023-07-07","arxiv_id":"2307.03672","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["atong01/conditional-flow-matching"],"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"]}}},{"paper":"/paper/unsupervised-3d-out-of-distribution-detection","slug":"unsupervised-3d-out-of-distribution-detection","title":"Unsupervised 3D out-of-distribution detection with latent diffusion models","date":"2023-07-07","arxiv_id":"2307.03777","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-critical-look-at-the-current-usage-of","title":"A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task","date":"2023-07-06","arxiv_id":"2307.02862","n_code_links":0,"syntology":null},{"paper":"/paper/applying-a-color-palette-with-local-control","slug":"applying-a-color-palette-with-local-control","title":"Dequantization and Color Transfer with Diffusion Models","date":"2023-07-06","arxiv_id":"2307.02698","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["vibe007/Dequantization_Diffusion_Models"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/bundle-specific-tractogram-distribution","slug":"bundle-specific-tractogram-distribution","title":"Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation","date":"2023-07-06","arxiv_id":"2307.02825","n_code_links":1,"syntology":null},{"paper":"/paper/how-to-detect-unauthorized-data-usages-in","slug":"how-to-detect-unauthorized-data-usages-in","title":"DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models","date":"2023-07-06","arxiv_id":"2307.03108","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhentingwang/diagnosis"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ipo-ldm-depth-aided-360-degree-indoor-rgb","title":"PanoDiffusion: 360-degree Panorama Outpainting via Diffusion","date":"2023-07-06","arxiv_id":"2307.03177","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-cultural-gap-in-text-to-image","title":"On the Cultural Gap in Text-to-Image Generation","date":"2023-07-06","arxiv_id":"2307.02971","n_code_links":0,"syntology":null},{"paper":null,"slug":"probabilistic-and-semantic-descriptions-of","title":"Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications","date":"2023-07-06","arxiv_id":"2307.02881","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-image-ldr-to-hdr-conversion-using","title":"Single Image LDR to HDR Conversion using Conditional Diffusion","date":"2023-07-06","arxiv_id":"2307.02814","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-images-generated-by-deep-diffusion","title":"Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality","date":"2023-07-05","arxiv_id":"2307.02347","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffflow-a-unified-sde-framework-for-score","title":"DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks","date":"2023-07-05","arxiv_id":"2307.02159","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-models-for-computational-design-at","slug":"diffusion-models-for-computational-design-at","title":"Automating Computational Design with Generative AI","date":"2023-07-05","arxiv_id":"2307.02511","n_code_links":1,"syntology":null},{"paper":null,"slug":"direct-segmentation-of-brain-white-matter","title":"Direct segmentation of brain white matter tracts in diffusion MRI","date":"2023-07-05","arxiv_id":"2307.02223","n_code_links":0,"syntology":null},{"paper":"/paper/dragondiffusion-enabling-drag-style","slug":"dragondiffusion-enabling-drag-style","title":"DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models","date":"2023-07-05","arxiv_id":"2307.02421","n_code_links":2,"syntology":null},{"paper":"/paper/llcaps-learning-to-illuminate-low-light","slug":"llcaps-learning-to-illuminate-low-light","title":"LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion","date":"2023-07-05","arxiv_id":"2307.02452","n_code_links":1,"syntology":null},{"paper":null,"slug":"monte-carlo-sampling-without-isoperimetry-a","title":"Reverse Diffusion Monte Carlo","date":"2023-07-05","arxiv_id":"2307.02037","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompting-diffusion-representations-for-cross","title":"Prompting Diffusion Representations for Cross-Domain Semantic Segmentation","date":"2023-07-05","arxiv_id":"2307.02138","n_code_links":0,"syntology":null},{"paper":null,"slug":"radiff-controllable-diffusion-models-for","title":"RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation","date":"2023-07-05","arxiv_id":"2307.02392","n_code_links":0,"syntology":null},{"paper":null,"slug":"svdm-single-view-diffusion-model-for-pseudo","title":"SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection","date":"2023-07-05","arxiv_id":"2307.02270","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-conservatism-diffusion-policies-in","title":"Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning","date":"2023-07-04","arxiv_id":"2307.01472","n_code_links":0,"syntology":null},{"paper":"/paper/collaborative-score-distillation-for","slug":"collaborative-score-distillation-for","title":"Collaborative Score Distillation for Consistent Visual Synthesis","date":"2023-07-04","arxiv_id":"2307.04787","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["subin-kim-cv/CSD"],"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"]}}},{"paper":"/paper/crossway-diffusion-improving-diffusion-based","slug":"crossway-diffusion-improving-diffusion-based","title":"Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning","date":"2023-07-04","arxiv_id":"2307.01849","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["lostxine/crossway_diffusion"],"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"]}}},{"paper":"/paper/disentanglement-in-a-gan-for-unconditional","slug":"disentanglement-in-a-gan-for-unconditional","title":"Disentanglement in a GAN for Unconditional Speech Synthesis","date":"2023-07-04","arxiv_id":"2307.01673","n_code_links":1,"syntology":null},{"paper":"/paper/dit-3d-exploring-plain-diffusion-transformers-1","slug":"dit-3d-exploring-plain-diffusion-transformers-1","title":"DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation","date":"2023-07-04","arxiv_id":"2307.01831","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"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) · 1 unverified","official":null}},{"paper":null,"slug":"hybrid-neural-diffeomorphic-flow-for-shape","title":"Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane","date":"2023-07-04","arxiv_id":"2307.01957","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-reconstruct-the-bubble","title":"Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion","date":"2023-07-04","arxiv_id":"2307.02496","n_code_links":0,"syntology":null},{"paper":null,"slug":"leat-towards-robust-deepfake-disruption-in","title":"LEAT: Towards Robust Deepfake Disruption in Real-World Scenarios via Latent Ensemble Attack","date":"2023-07-04","arxiv_id":"2307.01520","n_code_links":0,"syntology":null},{"paper":"/paper/nexus-sine-qua-non-essentially-connected","slug":"nexus-sine-qua-non-essentially-connected","title":"Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale","date":"2023-07-04","arxiv_id":"2307.01482","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-constrained-time-series-generation","title":"On the Constrained Time-Series Generation Problem","date":"2023-07-04","arxiv_id":"2307.01717","n_code_links":0,"syntology":null},{"paper":"/paper/protodiffusion-classifier-free-diffusion","slug":"protodiffusion-classifier-free-diffusion","title":"ProtoDiffusion: Classifier-Free Diffusion Guidance with Prototype Learning","date":"2023-07-04","arxiv_id":"2307.01924","n_code_links":1,"syntology":null},{"paper":"/paper/sdxl-improving-latent-diffusion-models-for","slug":"sdxl-improving-latent-diffusion-models-for","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","date":"2023-07-04","arxiv_id":"2307.01952","n_code_links":9,"syntology":{"ran":14,"of":27,"n_ran_checked":14,"n_instrument":0,"unverified":13,"pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 13 unverified","official":{"repos":["stability-ai/generative-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":10,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/swingnn-rethinking-permutation-invariance-in","slug":"swingnn-rethinking-permutation-invariance-in","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","date":"2023-07-04","arxiv_id":"2307.01646","n_code_links":2,"syntology":{"ran":8,"of":12,"n_ran_checked":5,"n_instrument":3,"unverified":4,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","official":{"repos":["qiyan98/swingnn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"synchronous-image-label-diffusion-probability","title":"Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT","date":"2023-07-04","arxiv_id":"2307.01740","n_code_links":0,"syntology":null}],"record_sha256":"2229bc87999f6cfe0ad4e075cb075075f017544cae30900fec114fbc51b0669a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}