{"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/111","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":111,"pages_in_order":139,"rows_per_page":100,"rows":[11001,11100],"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/110","next":"/method/diffusion/papers/112","papers":[{"paper":null,"slug":"training-energy-based-models-with-diffusion","title":"Training Energy-Based Models with Diffusion Contrastive Divergences","date":"2023-07-04","arxiv_id":"2307.01668","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-video-anomaly-detection-with","slug":"unsupervised-video-anomaly-detection-with","title":"Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations","date":"2023-07-04","arxiv_id":"2307.01533","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["anilosmantur/conditioned_video_anomaly_diffusion"],"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"]}}},{"paper":null,"slug":"acdmsr-accelerated-conditional-diffusion","title":"ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution","date":"2023-07-03","arxiv_id":"2307.00781","n_code_links":0,"syntology":null},{"paper":"/paper/diffss-diffusion-model-for-few-shot-semantic","slug":"diffss-diffusion-model-for-few-shot-semantic","title":"DifFSS: Diffusion Model for Few-Shot Semantic Segmentation","date":"2023-07-03","arxiv_id":"2307.00773","n_code_links":1,"syntology":null},{"paper":"/paper/imdiffusion-imputed-diffusion-models-for","slug":"imdiffusion-imputed-diffusion-models-for","title":"ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection","date":"2023-07-03","arxiv_id":"2307.00754","n_code_links":1,"syntology":null},{"paper":"/paper/improved-sampling-via-learned-diffusions","slug":"improved-sampling-via-learned-diffusions","title":"Improved sampling via learned diffusions","date":"2023-07-03","arxiv_id":"2307.01198","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"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","official":{"repos":["juliusberner/sde_sampler"],"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"]}}},{"paper":null,"slug":"investigating-data-memorization-in-3d-latent","title":"Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis","date":"2023-07-03","arxiv_id":"2307.01148","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-mixtures-of-gaussians-using-the-ddpm","title":"Learning Mixtures of Gaussians Using the DDPM Objective","date":"2023-07-03","arxiv_id":"2307.01178","n_code_links":0,"syntology":null},{"paper":"/paper/mvdiffusion-enabling-holistic-multi-view-1","slug":"mvdiffusion-enabling-holistic-multi-view-1","title":"MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion","date":"2023-07-03","arxiv_id":"2307.01097","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":5,"n_instrument":1,"unverified":4,"pointer_only":10,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["Tangshitao/MVDiffusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"patch-cnn-training-data-efficient-deep","title":"Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols","date":"2023-07-03","arxiv_id":"2307.01346","n_code_links":0,"syntology":null},{"paper":null,"slug":"squeezing-large-scale-diffusion-models-for","title":"Squeezing Large-Scale Diffusion Models for Mobile","date":"2023-07-03","arxiv_id":"2307.01193","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-manifold-learning-via-random","title":"Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities","date":"2023-07-03","arxiv_id":"2307.01077","n_code_links":0,"syntology":null},{"paper":"/paper/transport-variational-inference-and","slug":"transport-variational-inference-and","title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","date":"2023-07-03","arxiv_id":"2307.01050","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["shreyaspadhy/cmcd"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/3d-ids-doubly-disentangled-dynamic-intrusion","slug":"3d-ids-doubly-disentangled-dynamic-intrusion","title":"3D-IDS: Doubly Disentangled Dynamic Intrusion Detection","date":"2023-07-02","arxiv_id":"2307.11079","n_code_links":1,"syntology":null},{"paper":null,"slug":"bidirectional-temporal-diffusion-model-for","title":"Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation","date":"2023-07-02","arxiv_id":"2307.00574","n_code_links":0,"syntology":null},{"paper":null,"slug":"ledits-real-image-editing-with-ddpm-inversion","title":"LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance","date":"2023-07-02","arxiv_id":"2307.00522","n_code_links":0,"syntology":null},{"paper":null,"slug":"missdiff-training-diffusion-models-on-tabular","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values","date":"2023-07-02","arxiv_id":"2307.00467","n_code_links":0,"syntology":null},{"paper":"/paper/solving-linear-inverse-problems-provably-via-1","slug":"solving-linear-inverse-problems-provably-via-1","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","date":"2023-07-02","arxiv_id":"2307.00619","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["liturout/psld"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"variational-autoencoding-molecular-graphs","title":"Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model","date":"2023-07-02","arxiv_id":"2307.00623","n_code_links":0,"syntology":null},{"paper":"/paper/probvlm-probabilistic-adapter-for-frozen","slug":"probvlm-probabilistic-adapter-for-frozen","title":"ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models","date":"2023-07-01","arxiv_id":"2307.00398","n_code_links":1,"syntology":{"ran":8,"of":15,"n_ran_checked":5,"n_instrument":3,"unverified":7,"pointer_only":2,"phrase":"8 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; 3 where Syntology's instrument failed) · 7 unverified","official":{"repos":["explainableml/probvlm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/re-think-and-re-design-graph-neural-networks","slug":"re-think-and-re-design-graph-neural-networks","title":"Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals","date":"2023-07-01","arxiv_id":"2307.00222","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Dandy5721/GNN-PDE-COV"],"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":"/paper/residual-based-attention-and-connection-to","slug":"residual-based-attention-and-connection-to","title":"Residual-based attention and connection to information bottleneck theory in PINNs","date":"2023-07-01","arxiv_id":"2307.00379","n_code_links":1,"syntology":null},{"paper":null,"slug":"vesselmorph-domain-generalized-retinal-vessel","title":"VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation","date":"2023-07-01","arxiv_id":"2307.00240","n_code_links":0,"syntology":null},{"paper":null,"slug":"class-incremental-learning-using-diffusion","title":"Class-Incremental Learning using Diffusion Model for Distillation and Replay","date":"2023-06-30","arxiv_id":"2306.17560","n_code_links":0,"syntology":null},{"paper":null,"slug":"counting-guidance-for-high-fidelity-text-to","title":"Counting Guidance for High Fidelity Text-to-Image Synthesis","date":"2023-06-30","arxiv_id":"2306.17567","n_code_links":0,"syntology":null},{"paper":"/paper/magic123-one-image-to-high-quality-3d-object","slug":"magic123-one-image-to-high-quality-3d-object","title":"Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors","date":"2023-06-30","arxiv_id":"2306.17843","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["guochengqian/magic123"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-shaped-transformer-attention-models-in","title":"The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit","date":"2023-06-30","arxiv_id":"2306.17759","n_code_links":0,"syntology":null},{"paper":"/paper/diff-foley-synchronized-video-to-audio-1","slug":"diff-foley-synchronized-video-to-audio-1","title":"Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models","date":"2023-06-29","arxiv_id":"2306.17203","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/diffusion-jump-gnns-homophiliation-via","slug":"diffusion-jump-gnns-homophiliation-via","title":"Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters","date":"2023-06-29","arxiv_id":"2306.16976","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":["AhmedBegggaUA/TFM"],"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/diffusionstr-diffusion-model-for-scene-text","slug":"diffusionstr-diffusion-model-for-scene-text","title":"DiffusionSTR: Diffusion Model for Scene Text Recognition","date":"2023-06-29","arxiv_id":"2306.16707","n_code_links":0,"syntology":null},{"paper":"/paper/filtered-guided-diffusion-fast-filter","slug":"filtered-guided-diffusion-fast-filter","title":"Filtered-Guided Diffusion: Fast Filter Guidance for Black-Box Diffusion Models","date":"2023-06-29","arxiv_id":"2306.17141","n_code_links":1,"syntology":null},{"paper":null,"slug":"generate-anything-anywhere-in-any-scene","title":"Generate Anything Anywhere in Any Scene","date":"2023-06-29","arxiv_id":"2306.17154","n_code_links":0,"syntology":null},{"paper":"/paper/graph-denoising-diffusion-for-inverse-protein","slug":"graph-denoising-diffusion-for-inverse-protein","title":"Graph Denoising Diffusion for Inverse Protein Folding","date":"2023-06-29","arxiv_id":"2306.16819","n_code_links":1,"syntology":{"ran":18,"of":21,"n_ran_checked":15,"n_instrument":3,"unverified":3,"pointer_only":3,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 2 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ykiiiiii/grade_if"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"id-pose-sparse-view-camera-pose-estimation-by","title":"ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models","date":"2023-06-29","arxiv_id":"2306.17140","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-structure-guided-diffusion-model-for","title":"Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation","date":"2023-06-29","arxiv_id":"2306.17074","n_code_links":0,"syntology":null},{"paper":"/paper/michelangelo-conditional-3d-shape-generation-1","slug":"michelangelo-conditional-3d-shape-generation-1","title":"Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation","date":"2023-06-29","arxiv_id":"2306.17115","n_code_links":1,"syntology":null},{"paper":"/paper/one-2-3-45-any-single-image-to-3d-mesh-in-45-1","slug":"one-2-3-45-any-single-image-to-3d-mesh-in-45-1","title":"One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization","date":"2023-06-29","arxiv_id":"2306.16928","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"7 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["One-2-3-45/One-2-3-45"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/sagess-sampling-graph-denoising-diffusion","slug":"sagess-sampling-graph-denoising-diffusion","title":"SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation","date":"2023-06-29","arxiv_id":"2306.16827","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-mri-reconstruction-with","slug":"self-supervised-mri-reconstruction-with","title":"Self-Supervised MRI Reconstruction with Unrolled Diffusion Models","date":"2023-06-29","arxiv_id":"2306.16654","n_code_links":1,"syntology":null},{"paper":"/paper/spiking-denoising-diffusion-probabilistic","slug":"spiking-denoising-diffusion-probabilistic","title":"Spiking Denoising Diffusion Probabilistic Models","date":"2023-06-29","arxiv_id":"2306.17046","n_code_links":1,"syntology":null},{"paper":null,"slug":"twice-binnable-color-filter-arrays","title":"Double Binnable RGB, RGBW and LMS Color Filter Arrays","date":"2023-06-29","arxiv_id":"2306.17078","n_code_links":0,"syntology":null},{"paper":null,"slug":"asymptotic-preserving-convolutional-deeponets","title":"Capturing the Diffusive Behavior of the Multiscale Linear Transport Equations by Asymptotic-Preserving Convolutional DeepONets","date":"2023-06-28","arxiv_id":"2306.15891","n_code_links":0,"syntology":null},{"paper":null,"slug":"discovering-stochastic-partial-differential","title":"Discovering stochastic partial differential equations from limited data using variational Bayes inference","date":"2023-06-28","arxiv_id":"2306.15873","n_code_links":0,"syntology":null},{"paper":"/paper/dosediff-distance-aware-diffusion-model-for","slug":"dosediff-distance-aware-diffusion-model-for","title":"DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy","date":"2023-06-28","arxiv_id":"2306.16324","n_code_links":1,"syntology":{"ran":9,"of":13,"n_ran_checked":5,"n_instrument":4,"unverified":4,"pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["whisney/dosediff"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gexse-generative-explanatory-sensor-system-an","title":"GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces","date":"2023-06-28","arxiv_id":"2306.15857","n_code_links":0,"syntology":null},{"paper":"/paper/mydigitalfootprint-an-extensive-context","slug":"mydigitalfootprint-an-extensive-context","title":"MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge","date":"2023-06-28","arxiv_id":"2306.15990","n_code_links":1,"syntology":null},{"paper":"/paper/pfb-diff-progressive-feature-blending","slug":"pfb-diff-progressive-feature-blending","title":"PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing","date":"2023-06-28","arxiv_id":"2306.16894","n_code_links":1,"syntology":null},{"paper":null,"slug":"svnr-spatially-variant-noise-removal-with","title":"SVNR: Spatially-variant Noise Removal with Denoising Diffusion","date":"2023-06-28","arxiv_id":"2306.16052","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-training-for-graph-neural","title":"Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions","date":"2023-06-27","arxiv_id":"2306.15427","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximated-prompt-tuning-for-vision","title":"Approximated Prompt Tuning for Vision-Language Pre-trained Models","date":"2023-06-27","arxiv_id":"2306.15706","n_code_links":0,"syntology":null},{"paper":"/paper/easing-color-shifts-in-score-based-diffusion","slug":"easing-color-shifts-in-score-based-diffusion","title":"Easing Color Shifts in Score-Based Diffusion Models","date":"2023-06-27","arxiv_id":"2306.15832","n_code_links":1,"syntology":null},{"paper":"/paper/posediffusion-solving-pose-estimation-via","slug":"posediffusion-solving-pose-estimation-via","title":"PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment","date":"2023-06-27","arxiv_id":"2306.15667","n_code_links":1,"syntology":null},{"paper":"/paper/survival-extinction-and-interface-stability","slug":"survival-extinction-and-interface-stability","title":"Survival, extinction, and interface stability in a two--phase moving boundary model of biological invasion","date":"2023-06-27","arxiv_id":"2306.15379","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-star-test-time-attention-segregation-and","title":"A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis","date":"2023-06-26","arxiv_id":"2306.14544","n_code_links":0,"syntology":null},{"paper":"/paper/decompose-and-realign-tackling-condition","slug":"decompose-and-realign-tackling-condition","title":"Text-Anchored Score Composition: Tackling Condition Misalignment in Text-to-Image Diffusion Models","date":"2023-06-26","arxiv_id":"2306.14408","n_code_links":1,"syntology":null},{"paper":"/paper/diffsketcher-text-guided-vector-sketch","slug":"diffsketcher-text-guided-vector-sketch","title":"DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models","date":"2023-06-26","arxiv_id":"2306.14685","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ximinng/DiffSketcher"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/dragdiffusion-harnessing-diffusion-models-for","slug":"dragdiffusion-harnessing-diffusion-models-for","title":"DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing","date":"2023-06-26","arxiv_id":"2306.14435","n_code_links":4,"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: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Yujun-Shi/DragDiffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"elucidating-interfacial-dynamics-of-ti-al","title":"Elucidating Interfacial Dynamics of Ti-Al Systems Using Molecular Dynamics Simulation and Markov State Modeling","date":"2023-06-26","arxiv_id":"2306.14568","n_code_links":0,"syntology":null},{"paper":"/paper/fuzzy-conditioned-diffusion-and-diffusion","slug":"fuzzy-conditioned-diffusion-and-diffusion","title":"Fuzzy-Conditioned Diffusion and Diffusion Projection Attention Applied to Facial Image Correction","date":"2023-06-26","arxiv_id":"2306.14891","n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-unadjusted-langevin-methods-for-high","title":"Hybrid unadjusted Langevin methods for high-dimensional latent variable models","date":"2023-06-26","arxiv_id":"2306.14445","n_code_links":0,"syntology":null},{"paper":"/paper/model-contrastive-explanations-through","slug":"model-contrastive-explanations-through","title":"Model-contrastive explanations through symbolic reasoning","date":"2023-06-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/protodiff-learning-to-learn-prototypical-1","slug":"protodiff-learning-to-learn-prototypical-1","title":"ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion","date":"2023-06-26","arxiv_id":"2306.14770","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":["ydu-uva/protodiff"],"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","unlocated"]}}},{"paper":"/paper/restart-sampling-for-improving-generative","slug":"restart-sampling-for-improving-generative","title":"Restart Sampling for Improving Generative Processes","date":"2023-06-26","arxiv_id":"2306.14878","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"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) · 0 unverified","official":{"repos":["newbeeer/diffusion_restart_sampling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/cdiffmr-can-we-replace-the-gaussian-noise","slug":"cdiffmr-can-we-replace-the-gaussian-noise","title":"CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?","date":"2023-06-25","arxiv_id":"2306.14350","n_code_links":1,"syntology":null},{"paper":null,"slug":"diffmix-diffusion-model-based-data-synthesis","title":"DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets","date":"2023-06-25","arxiv_id":"2306.14132","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-model-based-low-light-image","title":"A ground-based dataset and a diffusion model for on-orbit low-light image enhancement","date":"2023-06-25","arxiv_id":"2306.14227","n_code_links":0,"syntology":null},{"paper":"/paper/domainstudio-fine-tuning-diffusion-models-for","slug":"domainstudio-fine-tuning-diffusion-models-for","title":"DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data","date":"2023-06-25","arxiv_id":"2306.14153","n_code_links":1,"syntology":null},{"paper":null,"slug":"diffdtm-a-conditional-structure-free","title":"DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins","date":"2023-06-24","arxiv_id":"2306.13957","n_code_links":0,"syntology":null},{"paper":null,"slug":"farthest-streamline-sampling-for-the-uniform","title":"Farthest Streamline Sampling for the Uniform Distribution of Forearm Muscle Fiber Tracts from Diffusion Tensor Imaging","date":"2023-06-24","arxiv_id":"2306.13969","n_code_links":0,"syntology":null},{"paper":null,"slug":"seeds-emulation-of-weather-forecast-ensembles","title":"SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models","date":"2023-06-24","arxiv_id":"2306.14066","n_code_links":0,"syntology":null},{"paper":"/paper/decoupled-diffusion-models-with-explicit","slug":"decoupled-diffusion-models-with-explicit","title":"Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation","date":"2023-06-23","arxiv_id":"2306.13720","n_code_links":2,"syntology":null},{"paper":"/paper/diffinfinite-large-mask-image-synthesis-via","slug":"diffinfinite-large-mask-image-synthesis-via","title":"DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology","date":"2023-06-23","arxiv_id":"2306.13384","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":10,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 3 honoured, 2 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["marcoaversa/diffinfinite"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/dreameditor-text-driven-3d-scene-editing-with","slug":"dreameditor-text-driven-3d-scene-editing-with","title":"DreamEditor: Text-Driven 3D Scene Editing with Neural Fields","date":"2023-06-23","arxiv_id":"2306.13455","n_code_links":1,"syntology":null},{"paper":null,"slug":"zero-shot-spatial-layout-conditioning-for","title":"Zero-shot spatial layout conditioning for text-to-image diffusion models","date":"2023-06-23","arxiv_id":"2306.13754","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-prior-regularized-full-waveform-inversion","title":"A prior regularized full waveform inversion using generative diffusion models","date":"2023-06-22","arxiv_id":"2306.12776","n_code_links":0,"syntology":null},{"paper":null,"slug":"continuous-layout-editing-of-single-images","title":"Continuous Layout Editing of Single Images with Diffusion Models","date":"2023-06-22","arxiv_id":"2306.13078","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffwa-diffusion-models-for-watermark-attack","title":"DiffWA: Diffusion Models for Watermark Attack","date":"2023-06-22","arxiv_id":"2306.12790","n_code_links":0,"syntology":null},{"paper":null,"slug":"dimsam-diffusion-models-as-samplers-for-task","title":"DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability","date":"2023-06-22","arxiv_id":"2306.13196","n_code_links":0,"syntology":null},{"paper":null,"slug":"directional-diffusion-models-for-graph","title":"Directional diffusion models for graph representation learning","date":"2023-06-22","arxiv_id":"2306.13210","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-at-a-time-multi-step-volumetric","title":"One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception","date":"2023-06-22","arxiv_id":"2306.12681","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-more-realistic-membership-inference","title":"Towards More Realistic Membership Inference Attacks on Large Diffusion Models","date":"2023-06-22","arxiv_id":"2306.12983","n_code_links":0,"syntology":null},{"paper":"/paper/ambigram-generation-by-a-diffusion-model","slug":"ambigram-generation-by-a-diffusion-model","title":"Ambigram Generation by A Diffusion Model","date":"2023-06-21","arxiv_id":"2306.12049","n_code_links":1,"syntology":null},{"paper":null,"slug":"corrector-operator-to-enhance-accuracy-and","title":"Residual-Based Error Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems","date":"2023-06-21","arxiv_id":"2306.12047","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffuseir-diffusion-models-for-isotropic","title":"DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images","date":"2023-06-21","arxiv_id":"2306.12109","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-posterior-sampling-for-informed","slug":"diffusion-posterior-sampling-for-informed","title":"Diffusion Posterior Sampling for Informed Single-Channel Dereverberation","date":"2023-06-21","arxiv_id":"2306.12286","n_code_links":1,"syntology":null},{"paper":null,"slug":"distributed-random-reshuffling-methods-with","title":"Distributed Random Reshuffling Methods with Improved Convergence","date":"2023-06-21","arxiv_id":"2306.12037","n_code_links":0,"syntology":null},{"paper":null,"slug":"dreamtime-an-improved-optimization-strategy","title":"DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation","date":"2023-06-21","arxiv_id":"2306.12422","n_code_links":0,"syntology":null},{"paper":null,"slug":"hsr-diff-hyperspectral-image-super-resolution","title":"HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models","date":"2023-06-21","arxiv_id":"2306.12085","n_code_links":0,"syntology":null},{"paper":"/paper/semi-implicit-denoising-diffusion-models","slug":"semi-implicit-denoising-diffusion-models","title":"Semi-Implicit Denoising Diffusion Models (SIDDMs)","date":"2023-06-21","arxiv_id":"2306.12511","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xuyanwu/SIDDMs-UFOGen"],"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","unlocated"]}}},{"paper":null,"slug":"taupetgen-text-conditional-tau-pet-image","title":"TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models","date":"2023-06-21","arxiv_id":"2306.11984","n_code_links":0,"syntology":null},{"paper":null,"slug":"align-adapt-and-inject-sound-guided-unified","title":"Align, Adapt and Inject: Sound-guided Unified Image Generation","date":"2023-06-20","arxiv_id":"2306.11504","n_code_links":0,"syntology":null},{"paper":null,"slug":"criteria-for-nupbr-nflvr-and-the-existence-of","title":"Criteria for the absence of arbitrage in general diffusion markets","date":"2023-06-20","arxiv_id":"2306.11470","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-with-forward-models-solving","slug":"diffusion-with-forward-models-solving","title":"Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision","date":"2023-06-20","arxiv_id":"2306.11719","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":null}},{"paper":null,"slug":"eliminating-lipschitz-singularities-in","title":"Lipschitz Singularities in Diffusion Models","date":"2023-06-20","arxiv_id":"2306.11251","n_code_links":0,"syntology":null},{"paper":null,"slug":"emog-synthesizing-emotive-co-speech-3d","title":"EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model","date":"2023-06-20","arxiv_id":"2306.11496","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-effectiveness-of-dataset","title":"Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards","date":"2023-06-20","arxiv_id":"2306.11763","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-profitable-nft-image-diffusions-via","title":"Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning","date":"2023-06-20","arxiv_id":"2306.11731","n_code_links":0,"syntology":null},{"paper":"/paper/masked-diffusion-models-are-fast-learners","slug":"masked-diffusion-models-are-fast-learners","title":"Masked Diffusion Models Are Fast Distribution Learners","date":"2023-06-20","arxiv_id":"2306.11363","n_code_links":1,"syntology":{"ran":11,"of":11,"n_ran_checked":9,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jiachenlei/maskdm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"pattern-formation-in-a-predator-prey-model","title":"Pattern formation in a predator-prey model with Allee effect and hyperbolic mortality on networked and non-networked environments","date":"2023-06-20","arxiv_id":"2306.11818","n_code_links":0,"syntology":null},{"paper":null,"slug":"reward-shaping-via-diffusion-process-in","title":"Reward Shaping via Diffusion Process in Reinforcement Learning","date":"2023-06-20","arxiv_id":"2306.11885","n_code_links":0,"syntology":null}],"record_sha256":"a13debed4c16e11ca070ed8daeafc5f8b2458d246474b74ae4a25041f771eeec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}