{"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/93","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":93,"pages_in_order":139,"rows_per_page":100,"rows":[9201,9300],"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/92","next":"/method/diffusion/papers/94","papers":[{"paper":"/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","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: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","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"]}}},{"paper":null,"slug":"improving-new-physics-searches-with-diffusion","title":"Improving new physics searches with diffusion models for event observables and jet constituents","date":"2023-12-15","arxiv_id":"2312.10130","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-motion-editing-with-natural","title":"Iterative Motion Editing with Natural Language","date":"2023-12-15","arxiv_id":"2312.11538","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-diffusion-models-with-image-derived","title":"Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology","date":"2023-12-15","arxiv_id":"2312.09792","n_code_links":0,"syntology":null},{"paper":"/paper/learning-distributions-on-manifolds-with-free","slug":"learning-distributions-on-manifolds-with-free","title":"Learning Distributions on Manifolds with Free-Form Flows","date":"2023-12-15","arxiv_id":"2312.09852","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["vislearn/fff"],"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"]}}},{"paper":null,"slug":"movement-primitive-diffusion-learning-gentle","title":"Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects","date":"2023-12-15","arxiv_id":"2312.10008","n_code_links":0,"syntology":null},{"paper":null,"slug":"mvhuman-tailoring-2d-diffusion-with-multi","title":"MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation","date":"2023-12-15","arxiv_id":"2312.10120","n_code_links":0,"syntology":null},{"paper":null,"slug":"plasticine3d-non-rigid-3d-editting-with-text","title":"Plasticine3D: 3D Non-Rigid Editing with Text Guidance by Multi-View Embedding Optimization","date":"2023-12-15","arxiv_id":"2312.10111","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/rich-human-feedback-for-text-to-image","slug":"rich-human-feedback-for-text-to-image","title":"Rich Human Feedback for Text-to-Image Generation","date":"2023-12-15","arxiv_id":"2312.10240","n_code_links":2,"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":["google-research/google-research"],"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":"single-pw-takes-a-shortcut-to-compound-pw-in","title":"Single PW takes a shortcut to compound PW in US imaging","date":"2023-12-15","arxiv_id":"2312.09514","n_code_links":0,"syntology":null},{"paper":"/paper/socio-economic-deprivation-analysis-diffusion","slug":"socio-economic-deprivation-analysis-diffusion","title":"Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps","date":"2023-12-15","arxiv_id":"2312.09830","n_code_links":1,"syntology":null},{"paper":null,"slug":"tell-me-what-you-see-text-guided-real-world","title":"Tell Me What You See: Text-Guided Real-World Image Denoising","date":"2023-12-15","arxiv_id":"2312.10191","n_code_links":0,"syntology":null},{"paper":"/paper/tf-clip-learning-text-free-clip-for-video","slug":"tf-clip-learning-text-free-clip-for-video","title":"TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification","date":"2023-12-15","arxiv_id":"2312.09627","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":3,"n_instrument":4,"unverified":3,"pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","official":{"repos":["asuradayuci/tf-clip"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-framework-for-conditional-diffusion","title":"A framework for conditional diffusion modelling with applications in motif scaffolding for protein design","date":"2023-12-14","arxiv_id":"2312.09236","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-generalized-neural-diffusion-framework-on","title":"A Generalized Neural Diffusion Framework on Graphs","date":"2023-12-14","arxiv_id":"2312.08616","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-sparse-cross-attention-based-graph","title":"A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction","date":"2023-12-14","arxiv_id":"2312.09050","n_code_links":0,"syntology":null},{"paper":"/paper/agent-attention-on-the-integration-of-softmax","slug":"agent-attention-on-the-integration-of-softmax","title":"Agent Attention: On the Integration of Softmax and Linear Attention","date":"2023-12-14","arxiv_id":"2312.08874","n_code_links":2,"syntology":{"ran":13,"of":19,"n_ran_checked":9,"n_instrument":4,"unverified":6,"pointer_only":19,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","official":{"repos":["leaplabthu/agent-attention"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"brain-diffuser-with-hierarchical-transformer","title":"BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment","date":"2023-12-14","arxiv_id":"2312.09022","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-c-unveiling-the-generative","title":"Diffusion-C: Unveiling the Generative Challenges of Diffusion Models through Corrupted Data","date":"2023-12-14","arxiv_id":"2312.08843","n_code_links":0,"syntology":null},{"paper":"/paper/diffusionlight-light-probes-for-free-by","slug":"diffusionlight-light-probes-for-free-by","title":"DiffusionLight: Light Probes for Free by Painting a Chrome Ball","date":"2023-12-14","arxiv_id":"2312.09168","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":4,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["DiffusionLight/DiffusionLight"],"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"]}}},{"paper":null,"slug":"dreamdrone","title":"DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators","date":"2023-12-14","arxiv_id":"2312.08746","n_code_links":0,"syntology":null},{"paper":"/paper/fast-sampling-via-de-randomization-for","slug":"fast-sampling-via-de-randomization-for","title":"Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time","date":"2023-12-14","arxiv_id":"2312.09193","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["uclaml/dndm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"finecontrolnet-fine-level-text-control-for","title":"FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection","date":"2023-12-14","arxiv_id":"2312.09252","n_code_links":0,"syntology":null},{"paper":null,"slug":"goenfusion-gradient-origin-encodings-for-3d","title":"GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning","date":"2023-12-14","arxiv_id":"2312.08744","n_code_links":0,"syntology":null},{"paper":null,"slug":"guided-diffusion-from-self-supervised","title":"Guided Diffusion from Self-Supervised Diffusion Features","date":"2023-12-14","arxiv_id":"2312.08825","n_code_links":0,"syntology":null},{"paper":"/paper/improving-efficiency-of-diffusion-models-via","slug":"improving-efficiency-of-diffusion-models-via","title":"Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures","date":"2023-12-14","arxiv_id":"2312.09181","n_code_links":1,"syntology":null},{"paper":null,"slug":"joint2human-high-quality-3d-human-generation","title":"Joint2Human: High-quality 3D Human Generation via Compact Spherical Embedding of 3D Joints","date":"2023-12-14","arxiv_id":"2312.08591","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"lime-localized-image-editing-via-attention","title":"LIME: Localized Image Editing via Attention Regularization in Diffusion Models","date":"2023-12-14","arxiv_id":"2312.09256","n_code_links":0,"syntology":null},{"paper":null,"slug":"mosaic-sdf-for-3d-generative-models","title":"Mosaic-SDF for 3D Generative Models","date":"2023-12-14","arxiv_id":"2312.09222","n_code_links":0,"syntology":null},{"paper":null,"slug":"motion-flow-matching-for-human-motion","title":"Motion Flow Matching for Human Motion Synthesis and Editing","date":"2023-12-14","arxiv_id":"2312.08895","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-latent-space-learning-for-chain","title":"Multi-modal Latent Space Learning for Chain-of-Thought Reasoning in Language Models","date":"2023-12-14","arxiv_id":"2312.08762","n_code_links":0,"syntology":null},{"paper":null,"slug":"omg-towards-open-vocabulary-motion-generation","title":"OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers","date":"2023-12-14","arxiv_id":"2312.08985","n_code_links":0,"syntology":null},{"paper":null,"slug":"pi3d-efficient-text-to-3d-generation-with","title":"PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion","date":"2023-12-14","arxiv_id":"2312.09069","n_code_links":0,"syntology":null},{"paper":null,"slug":"planning-and-rendering-towards-end-to-end","title":"Planning and Rendering: Towards Product Poster Generation with Diffusion Models","date":"2023-12-14","arxiv_id":"2312.08822","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconstruction-of-sound-field-through","title":"Reconstruction of Sound Field through Diffusion Models","date":"2023-12-14","arxiv_id":"2312.08821","n_code_links":0,"syntology":null},{"paper":"/paper/reliability-in-semantic-segmentation-can-we","slug":"reliability-in-semantic-segmentation-can-we","title":"Reliability in Semantic Segmentation: Can We Use Synthetic Data?","date":"2023-12-14","arxiv_id":"2312.09231","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["valeoai/genval"],"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":null,"slug":"single-mesh-diffusion-models-with-field","title":"Single Mesh Diffusion Models with Field Latents for Texture Generation","date":"2023-12-14","arxiv_id":"2312.09250","n_code_links":0,"syntology":null},{"paper":"/paper/speeding-up-photoacoustic-imaging-using","slug":"speeding-up-photoacoustic-imaging-using","title":"Speeding up Photoacoustic Imaging using Diffusion Models","date":"2023-12-14","arxiv_id":"2312.08834","n_code_links":1,"syntology":null},{"paper":null,"slug":"text2immersion-generative-immersive-scene","title":"Text2Immersion: Generative Immersive Scene with 3D Gaussians","date":"2023-12-14","arxiv_id":"2312.09242","n_code_links":0,"syntology":null},{"paper":"/paper/triplane-meets-gaussian-splatting-fast-and","slug":"triplane-meets-gaussian-splatting-fast-and","title":"Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers","date":"2023-12-14","arxiv_id":"2312.09147","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"unbiasing-enhanced-sampling-on-a-high","title":"Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model","date":"2023-12-14","arxiv_id":"2312.09404","n_code_links":0,"syntology":null},{"paper":null,"slug":"unidream-unifying-diffusion-priors-for","title":"UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation","date":"2023-12-14","arxiv_id":"2312.08754","n_code_links":0,"syntology":null},{"paper":null,"slug":"valid-variable-length-input-diffusion-for","title":"VaLID: Variable-Length Input Diffusion for Novel View Synthesis","date":"2023-12-14","arxiv_id":"2312.08892","n_code_links":0,"syntology":null},{"paper":"/paper/videolcm-video-latent-consistency-model","slug":"videolcm-video-latent-consistency-model","title":"VideoLCM: Video Latent Consistency Model","date":"2023-12-14","arxiv_id":"2312.09109","n_code_links":2,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/adapedit-spatio-temporal-guided-adaptive","slug":"adapedit-spatio-temporal-guided-adaptive","title":"AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for Text-Based Continuity-Sensitive Image Editing","date":"2023-12-13","arxiv_id":"2312.08019","n_code_links":1,"syntology":null},{"paper":"/paper/both2hands-inferring-3d-hands-from-both-text","slug":"both2hands-inferring-3d-hands-from-both-text","title":"BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics","date":"2023-12-13","arxiv_id":"2312.07937","n_code_links":1,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"clusterddpm-an-em-clustering-framework-with","title":"ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models","date":"2023-12-13","arxiv_id":"2312.08029","n_code_links":0,"syntology":null},{"paper":"/paper/concept-centric-personalization-with-large","slug":"concept-centric-personalization-with-large","title":"Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation","date":"2023-12-13","arxiv_id":"2312.08195","n_code_links":2,"syntology":null},{"paper":null,"slug":"denoising-diffusion-based-synthetic","title":"Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs","date":"2023-12-13","arxiv_id":"2312.07832","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffuseraw-end-to-end-generative-raw-image","title":"DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images","date":"2023-12-13","arxiv_id":"2402.18575","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-based-blind-text-image-super","slug":"diffusion-based-blind-text-image-super","title":"Diffusion-based Blind Text Image Super-Resolution","date":"2023-12-13","arxiv_id":"2312.08886","n_code_links":1,"syntology":null},{"paper":null,"slug":"diffusion-models-enable-zero-shot-pose","title":"Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users","date":"2023-12-13","arxiv_id":"2312.07854","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-nerf2nerf-streamlining-text-driven","title":"Efficient-NeRF2NeRF: Streamlining Text-Driven 3D Editing with Multiview Correspondence-Enhanced Diffusion Models","date":"2023-12-13","arxiv_id":"2312.08563","n_code_links":0,"syntology":null},{"paper":"/paper/evp-enhanced-visual-perception-using-inverse","slug":"evp-enhanced-visual-perception-using-inverse","title":"EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment","date":"2023-12-13","arxiv_id":"2312.08548","n_code_links":1,"syntology":null},{"paper":"/paper/facetalk-audio-driven-motion-diffusion-for","slug":"facetalk-audio-driven-motion-diffusion-for","title":"FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models","date":"2023-12-13","arxiv_id":"2312.08459","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-sampling-through-the-reuse-of-attention","title":"Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models","date":"2023-12-13","arxiv_id":"2401.01008","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-aware-artifact-image-synthesis-with","title":"Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision","date":"2023-12-13","arxiv_id":"2312.08056","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"nvist-in-the-wild-new-view-synthesis-from-a","title":"NViST: In the Wild New View Synthesis from a Single Image with Transformers","date":"2023-12-13","arxiv_id":"2312.08568","n_code_links":0,"syntology":null},{"paper":null,"slug":"permod-perceptually-grounded-voice","title":"PerMod: Perceptually Grounded Voice Modification with Latent Diffusion Models","date":"2023-12-13","arxiv_id":"2312.08494","n_code_links":0,"syntology":null},{"paper":"/paper/phendiff-revealing-invisible-phenotypes-with","slug":"phendiff-revealing-invisible-phenotypes-with","title":"PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images","date":"2023-12-13","arxiv_id":"2312.08290","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":8,"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) · 3 unverified","official":{"repos":["warmongeringbeaver/phendiff"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/pnpnet-pull-and-push-networks-for-volumetric","slug":"pnpnet-pull-and-push-networks-for-volumetric","title":"PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion","date":"2023-12-13","arxiv_id":"2312.08323","n_code_links":1,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"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","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"]}}},{"paper":null,"slug":"scenewiz3d-towards-text-guided-3d-scene","title":"SceneWiz3D: Towards Text-guided 3D Scene Composition","date":"2023-12-13","arxiv_id":"2312.08885","n_code_links":0,"syntology":null},{"paper":"/paper/score-based-diffusion-priors-for-multi-target","slug":"score-based-diffusion-priors-for-multi-target","title":"Score-based diffusion priors for multi-target detection","date":"2023-12-13","arxiv_id":"2312.08500","n_code_links":1,"syntology":null},{"paper":null,"slug":"seeavatar-photorealistic-text-to-3d-avatar","title":"SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance","date":"2023-12-13","arxiv_id":"2312.08889","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-aware-data-augmentation-for-text-to","slug":"semantic-aware-data-augmentation-for-text-to","title":"Semantic-aware Data Augmentation for Text-to-image Synthesis","date":"2023-12-13","arxiv_id":"2312.07951","n_code_links":1,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":7,"n_instrument":6,"unverified":1,"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","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"]}}},{"paper":"/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","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":1,"n_instrument":1,"unverified":4,"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","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"]}}},{"paper":"/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","n_code_links":1,"syntology":{"ran":12,"of":13,"n_ran_checked":12,"n_instrument":0,"unverified":1,"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","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"]}}},{"paper":null,"slug":"speedupnet-a-plug-and-play-hyper-network-for","title":"SpeedUpNet: A Plug-and-Play Adapter Network for Accelerating Text-to-Image Diffusion Models","date":"2023-12-13","arxiv_id":"2312.08887","n_code_links":0,"syntology":null},{"paper":null,"slug":"stable-rivers-a-case-study-in-the-application","title":"Stable Rivers: A Case Study in the Application of Text-to-Image Generative Models for Earth Sciences","date":"2023-12-13","arxiv_id":"2312.07833","n_code_links":0,"syntology":null},{"paper":null,"slug":"time-series-diffusion-method-a-denoising","title":"Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation","date":"2023-12-13","arxiv_id":"2312.07981","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"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","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"]}}},{"paper":null,"slug":"a-unified-sampling-framework-for-solver","title":"A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models","date":"2023-12-12","arxiv_id":"2312.07243","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-latent-diffusion-with-flow-matching","slug":"boosting-latent-diffusion-with-flow-matching","title":"Boosting Latent Diffusion with Flow Matching","date":"2023-12-12","arxiv_id":"2312.07360","n_code_links":2,"syntology":null},{"paper":"/paper/brain-optimized-inference-improves","slug":"brain-optimized-inference-improves","title":"Brain-optimized inference improves reconstructions of fMRI brain activity","date":"2023-12-12","arxiv_id":"2312.07705","n_code_links":1,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["reesekneeland/Second-Sight"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ccm-adding-conditional-controls-to-text-to","title":"CCM: Adding Conditional Controls to Text-to-Image Consistency Models","date":"2023-12-12","arxiv_id":"2312.06971","n_code_links":0,"syntology":null},{"paper":null,"slug":"cosmological-field-emulation-and-parameter","title":"Cosmological Field Emulation and Parameter Inference with Diffusion Models","date":"2023-12-12","arxiv_id":"2312.07534","n_code_links":0,"syntology":null},{"paper":"/paper/diff-op3d-bridging-2d-diffusion-for-open-pose","slug":"diff-op3d-bridging-2d-diffusion-for-open-pose","title":"Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges","date":"2023-12-12","arxiv_id":"2312.07039","n_code_links":2,"syntology":null},{"paper":"/paper/diffmorpher-unleashing-the-capability-of","slug":"diffmorpher-unleashing-the-capability-of","title":"DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing","date":"2023-12-12","arxiv_id":"2312.07409","n_code_links":1,"syntology":null},{"paper":"/paper/diffusion-cocktail-fused-generation-from","slug":"diffusion-cocktail-fused-generation-from","title":"Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image Generations","date":"2023-12-12","arxiv_id":"2312.08873","n_code_links":1,"syntology":null},{"paper":"/paper/equivariant-flow-matching-with-hybrid-1","slug":"equivariant-flow-matching-with-hybrid-1","title":"Equivariant Flow Matching with Hybrid Probability Transport","date":"2023-12-12","arxiv_id":"2312.07168","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":1,"n_instrument":2,"unverified":2,"pointer_only":5,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["algomole/molfm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fast-training-of-diffusion-transformer-with","title":"Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation","date":"2023-12-12","arxiv_id":"2312.07231","n_code_links":0,"syntology":null},{"paper":null,"slug":"freecontrol-training-free-spatial-control-of","title":"FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition","date":"2023-12-12","arxiv_id":"2312.07536","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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","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"]}}},{"paper":null,"slug":"generating-high-resolution-regional","title":"Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model","date":"2023-12-12","arxiv_id":"2312.07112","n_code_links":0,"syntology":null},{"paper":"/paper/genhowto-learning-to-generate-actions-and","slug":"genhowto-learning-to-generate-actions-and","title":"GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos","date":"2023-12-12","arxiv_id":"2312.07322","n_code_links":1,"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":["soCzech/GenHowto"],"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":null,"slug":"inferring-interaction-networks-from","title":"Inferring interaction networks from transcriptomic data: methods and applications","date":"2023-12-12","arxiv_id":"2312.07012","n_code_links":0,"syntology":null},{"paper":"/paper/learned-representation-guided-diffusion","slug":"learned-representation-guided-diffusion","title":"Learned representation-guided diffusion models for large-image generation","date":"2023-12-12","arxiv_id":"2312.07330","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":11,"n_instrument":2,"unverified":1,"pointer_only":14,"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) · 1 unverified","official":{"repos":["cvlab-stonybrook/large-image-diffusion"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"lora-enhanced-distillation-on-guided","title":"LoRA-Enhanced Distillation on Guided Diffusion Models","date":"2023-12-12","arxiv_id":"2312.06899","n_code_links":0,"syntology":null},{"paper":null,"slug":"mind-3d-reconstruct-high-quality-3d-objects","title":"MinD-3D: Reconstruct High-quality 3D objects in Human Brain","date":"2023-12-12","arxiv_id":"2312.07485","n_code_links":0,"syntology":null},{"paper":null,"slug":"noise-distribution-decomposition-based-multi","title":"Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning","date":"2023-12-12","arxiv_id":"2312.07025","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-notion-of-hallucinations-from-the-lens","title":"On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images","date":"2023-12-12","arxiv_id":"2312.06979","n_code_links":0,"syntology":null},{"paper":"/paper/one-step-diffusion-distillation-via-deep-1","slug":"one-step-diffusion-distillation-via-deep-1","title":"One-Step Diffusion Distillation via Deep Equilibrium Models","date":"2023-12-12","arxiv_id":"2401.08639","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["locuslab/get"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scalable-motion-style-transfer-with","title":"Scalable Motion Style Transfer with Constrained Diffusion Generation","date":"2023-12-12","arxiv_id":"2312.07311","n_code_links":0,"syntology":null},{"paper":null,"slug":"template-free-reconstruction-of-human-object","title":"Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation","date":"2023-12-12","arxiv_id":"2312.07063","n_code_links":0,"syntology":null}],"record_sha256":"7af7982de69facc6122be0f13ed6da63a03270f72484f933cf230028b16fe54d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}