{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/denoising/papers/33","list_of":"/task/denoising","task":"Denoising","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":33,"pages_in_order":73,"rows_per_page":100,"rows":[3201,3300],"of":7282,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/denoising","prev":"/task/denoising/papers/32","next":"/task/denoising/papers/34","papers":[{"url":"/paper/thermalizer-stable-autoregressive-neural","slug":"thermalizer-stable-autoregressive-neural","title":"Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos","date":"2025-03-24","arxiv_id":"2503.18731","repositories_listed":0,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/thermalizer-stable-autoregressive-neural#ran","syntology_url":"https://syntology.ai/paper/2503.18731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.18731"}},"official":null}},{"url":null,"slug":"training-free-diffusion-acceleration-with","title":"Training-free Diffusion Acceleration with Bottleneck Sampling","date":"2025-03-24","arxiv_id":"2503.18940","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-uncertainty-aware-diffusion-for-multi","title":"Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling","date":"2025-03-24","arxiv_id":"2503.18589","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-architectures-for-the-learning-of","title":"Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers","date":"2025-03-24","arxiv_id":"2503.19190","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-t1-test-time-scaling-for-video","title":"Video-T1: Test-Time Scaling for Video Generation","date":"2025-03-24","arxiv_id":"2503.18942","repositories_listed":0,"syntology":null},{"url":null,"slug":"snraware-improved-deep-learning-mri-denoising","title":"SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation","date":"2025-03-23","arxiv_id":"2503.18162","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-geometry-and-color-compression","title":"Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors","date":"2025-03-23","arxiv_id":"2503.18083","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-foundation-model-priors-and","title":"Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction","date":"2025-03-22","arxiv_id":"2503.17788","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-diffusion-training-through","title":"Efficient Diffusion Training through Parallelization with Truncated Karhunen-Loève Expansion","date":"2025-03-22","arxiv_id":"2503.17657","repositories_listed":0,"syntology":null},{"url":null,"slug":"fractal-ir-a-unified-framework-for-efficient","title":"Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration","date":"2025-03-22","arxiv_id":"2503.17825","repositories_listed":0,"syntology":null},{"url":null,"slug":"guidance-free-image-editing-via-explicit","title":"Guidance Free Image Editing via Explicit Conditioning","date":"2025-03-22","arxiv_id":"2503.17593","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-transformer-based-aligned-generation","title":"Towards Transformer-Based Aligned Generation with Self-Coherence Guidance","date":"2025-03-22","arxiv_id":"2503.17675","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-ide-agent-integrated-denoising-experts","title":"A-IDE : Agent-Integrated Denoising Experts","date":"2025-03-21","arxiv_id":"2503.16780","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-efficacy-of-partial-denoising","title":"Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques","date":"2025-03-21","arxiv_id":"2503.17030","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-fast-and-slow-scalable-parallel","title":"Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks","date":"2025-03-21","arxiv_id":"2503.17539","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovering-pulse-waves-from-video-using-deep","title":"Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models","date":"2025-03-21","arxiv_id":"2503.17269","repositories_listed":0,"syntology":null},{"url":null,"slug":"blockdance-reuse-structurally-similar-spatio","title":"BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers","date":"2025-03-20","arxiv_id":"2503.15927","repositories_listed":0,"syntology":null},{"url":null,"slug":"fed-ndif-a-noise-embedded-federated-diffusion","title":"Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising","date":"2025-03-20","arxiv_id":"2503.16635","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-based-learning-of-adaptive-total","title":"Patch-based learning of adaptive Total Variation parameter maps for blind image denoising","date":"2025-03-20","arxiv_id":"2503.16010","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-wise-distillation-of-diffusion-models","title":"Scale-wise Distillation of Diffusion Models","date":"2025-03-20","arxiv_id":"2503.16397","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalingnoise-scaling-inference-time-search","title":"ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos","date":"2025-03-20","arxiv_id":"2503.16400","repositories_listed":0,"syntology":null},{"url":null,"slug":"scenemi-motion-in-betweening-for-modeling","title":"SceneMI: Motion In-betweening for Modeling Human-Scene Interactions","date":"2025-03-20","arxiv_id":"2503.16289","repositories_listed":0,"syntology":null},{"url":null,"slug":"shining-yourself-high-fidelity-ornaments","title":"Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model","date":"2025-03-20","arxiv_id":"2503.16065","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-score-analysis-for-understanding-and","title":"Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts","date":"2025-03-20","arxiv_id":"2503.16218","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-extension-of-noisy-target","title":"Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement","date":"2025-03-19","arxiv_id":"2503.14854","repositories_listed":0,"syntology":null},{"url":null,"slug":"fundamental-limits-of-matrix-sensing-exact","title":"Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications","date":"2025-03-18","arxiv_id":"2503.14121","repositories_listed":0,"syntology":null},{"url":null,"slug":"magiccomp-training-free-dual-phase-refinement","title":"MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation","date":"2025-03-18","arxiv_id":"2503.14428","repositories_listed":0,"syntology":null},{"url":null,"slug":"mosaic-generating-consistent-privacy","title":"MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments","date":"2025-03-18","arxiv_id":"2503.13816","repositories_listed":0,"syntology":null},{"url":null,"slug":"revealing-higher-order-neural-representations","title":"Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model","date":"2025-03-18","arxiv_id":"2503.14333","repositories_listed":0,"syntology":null},{"url":null,"slug":"sir-diff-sparse-image-sets-restoration-with","title":"SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model","date":"2025-03-18","arxiv_id":"2503.14463","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketchfusion-learning-universal-sketch","title":"SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models","date":"2025-03-18","arxiv_id":"2503.14129","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpc-a-single-diffusion-model-for-point","title":"SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization","date":"2025-03-18","arxiv_id":"2503.14558","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-design-of-denser-graph-frequency-graph","title":"A Design of Denser-Graph-Frequency Graph Fourier Frames for Graph Signal Analysis","date":"2025-03-17","arxiv_id":"2503.13164","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomically-and-metabolically-informed","title":"Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging","date":"2025-03-17","arxiv_id":"2503.13257","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-head-to-tail-towards-balanced","title":"From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration","date":"2025-03-17","arxiv_id":"2503.12821","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-denoising-in-score-based-generative","title":"Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity","date":"2025-03-17","arxiv_id":"2503.12966","repositories_listed":0,"syntology":null},{"url":null,"slug":"pandora-diffusion-policy-learning-for","title":"PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing","date":"2025-03-17","arxiv_id":"2503.14545","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalize-anything-for-free-with-diffusion","title":"Personalize Anything for Free with Diffusion Transformer","date":"2025-03-16","arxiv_id":"2503.12590","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-fourier-diffusion-language-model-sfdlm","title":"State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling","date":"2025-03-16","arxiv_id":"2503.17382","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffgap-a-lightweight-diffusion-module-in","title":"DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap","date":"2025-03-15","arxiv_id":"2503.12131","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-3d-gaussian-splatting-editing-with","title":"Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information","date":"2025-03-14","arxiv_id":"2503.11601","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-deep-speech-denoising-models-robust-to","title":"Are Deep Speech Denoising Models Robust to Adversarial Noise?","date":"2025-03-14","arxiv_id":"2503.11627","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-denoising-score-matching-to-langevin","title":"From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting","date":"2025-03-14","arxiv_id":"2503.11615","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-synthesis-for-low-light-image-denoising","title":"Noise Synthesis for Low-Light Image Denoising with Diffusion Models","date":"2025-03-14","arxiv_id":"2503.11262","repositories_listed":0,"syntology":null},{"url":null,"slug":"psf-4d-a-progressive-sampling-framework-for","title":"PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing","date":"2025-03-14","arxiv_id":"2503.11044","repositories_listed":0,"syntology":null},{"url":null,"slug":"watch-and-learn-leveraging-expert-knowledge","title":"Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding","date":"2025-03-14","arxiv_id":"2503.11392","repositories_listed":0,"syntology":null},{"url":null,"slug":"codiphy-a-general-framework-for-applying","title":"CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems","date":"2025-03-13","arxiv_id":"2503.10297","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybridvla-collaborative-diffusion-and","title":"HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model","date":"2025-03-13","arxiv_id":"2503.10631","repositories_listed":0,"syntology":null},{"url":null,"slug":"roodi-reconstructing-occluded-objects-with","title":"ROODI: Reconstructing Occluded Objects with Denoising Inpainters","date":"2025-03-13","arxiv_id":"2503.10256","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsr-nf-neural-field-regularization-by-static","title":"RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging","date":"2025-03-13","arxiv_id":"2503.10015","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-dictionary-learning-for-image-recovery","title":"Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage","date":"2025-03-13","arxiv_id":"2503.10732","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-graph-diffusion-policy-with","title":"Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation","date":"2025-03-13","arxiv_id":"2503.10743","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-classifier-free-guidance-from-a","title":"Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective","date":"2025-03-13","arxiv_id":"2503.10638","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2edit-versatile-video-diffusion-editor-for","title":"V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes","date":"2025-03-13","arxiv_id":"2503.10634","repositories_listed":0,"syntology":null},{"url":null,"slug":"videomerge-towards-training-free-long-video","title":"VideoMerge: Towards Training-free Long Video Generation","date":"2025-03-13","arxiv_id":"2503.09926","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-diffusion-sampling-via","title":"Accelerating Diffusion Sampling via Exploiting Local Transition Coherence","date":"2025-03-12","arxiv_id":"2503.09675","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-position-encoding-in-diffusion-u","title":"Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation","date":"2025-03-12","arxiv_id":"2503.09830","repositories_listed":0,"syntology":null},{"url":null,"slug":"incomplete-multi-view-clustering-via-2","title":"Incomplete Multi-view Clustering via Diffusion Contrastive Generation","date":"2025-03-12","arxiv_id":"2503.09185","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise2score3d-tweedie-s-approach-for","title":"Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising","date":"2025-03-12","arxiv_id":"2503.09283","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-via-repainting-an-image-denoising","title":"Denoising via Repainting: an image denoising method using layer wise medical image repainting","date":"2025-03-11","arxiv_id":"2503.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-ppg-a-gpt-based-foundation-model-for","title":"GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals","date":"2025-03-11","arxiv_id":"2503.08015","repositories_listed":0,"syntology":null},{"url":null,"slug":"posterior-mean-denoising-diffusion-model-for","title":"Posterior-Mean Denoising Diffusion Model for Realistic PET Image Reconstruction","date":"2025-03-11","arxiv_id":"2503.08546","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruct-anything-model-a-lightweight","title":"Reconstruct Anything Model: a lightweight foundation model for computational imaging","date":"2025-03-11","arxiv_id":"2503.08915","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-hamiltonian-network-for-physical","title":"Denoising Hamiltonian Network for Physical Reasoning","date":"2025-03-10","arxiv_id":"2503.07596","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-score-distillation-from-noisy","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","date":"2025-03-10","arxiv_id":"2503.07578","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-method-for-aerodynamic","title":"Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model","date":"2025-03-10","arxiv_id":"2503.07056","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-chirp-signal-and-graph-fractional","title":"Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution","date":"2025-03-10","arxiv_id":"2503.06981","repositories_listed":0,"syntology":null},{"url":null,"slug":"inversion-free-video-style-transfer-with","title":"Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging","date":"2025-03-10","arxiv_id":"2503.07363","repositories_listed":0,"syntology":null},{"url":null,"slug":"latexblend-scaling-multi-concept-customized","title":"LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending","date":"2025-03-10","arxiv_id":"2503.06956","repositories_listed":0,"syntology":null},{"url":null,"slug":"miga-mutual-information-guided-attack-on","title":"MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation","date":"2025-03-10","arxiv_id":"2503.06966","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-training-quantization-for-diffusion","title":"Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping","date":"2025-03-10","arxiv_id":"2503.06930","repositories_listed":0,"syntology":null},{"url":null,"slug":"tide-temporal-aware-sparse-autoencoders-for","title":"TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation","date":"2025-03-10","arxiv_id":"2503.07050","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-deep-denoising-with-self-guided","title":"Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images","date":"2025-03-10","arxiv_id":"2503.06827","repositories_listed":0,"syntology":null},{"url":null,"slug":"whiteness-based-bilevel-estimation-of","title":"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising","date":"2025-03-10","arxiv_id":"2503.07814","repositories_listed":0,"syntology":null},{"url":null,"slug":"d3dr-lighting-aware-object-insertion-in","title":"D3DR: Lighting-Aware Object Insertion in Gaussian Splatting","date":"2025-03-09","arxiv_id":"2503.06740","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-model-based-probabilistic-day-ahead","title":"Diffusion Model Based Probabilistic Day-ahead Load Forecasting","date":"2025-03-09","arxiv_id":"2503.06697","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-diffusion-models","title":"Federated Learning for Diffusion Models","date":"2025-03-09","arxiv_id":"2503.06426","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixelponder-dynamic-patch-adaptation-for","title":"PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation","date":"2025-03-09","arxiv_id":"2503.06684","repositories_listed":0,"syntology":null},{"url":null,"slug":"prose-diffusion-priors-for-speech-enhancement","title":"ProSE: Diffusion Priors for Speech Enhancement","date":"2025-03-09","arxiv_id":"2503.06375","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-audio-generation-from-dynamic-mri-via","title":"Speech Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder","date":"2025-03-09","arxiv_id":"2503.06588","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-synthetic-image-detection-through","title":"Explainable Synthetic Image Detection through Diffusion Timestep Ensembling","date":"2025-03-08","arxiv_id":"2503.06201","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointdiffuse-a-dual-conditional-diffusion","title":"PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation","date":"2025-03-08","arxiv_id":"2503.06094","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-denoising-and-convergent","title":"Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling","date":"2025-03-07","arxiv_id":"2503.05956","repositories_listed":0,"syntology":null},{"url":null,"slug":"magicinfinite-generating-infinite-talking","title":"MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice","date":"2025-03-07","arxiv_id":"2503.05978","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalist-cross-domain-molecular-learning","title":"A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery","date":"2025-03-06","arxiv_id":"2503.04362","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-is-the-best-learner-ct-free-ultra-low","title":"Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning","date":"2025-03-05","arxiv_id":"2503.03786","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-text-hallucination-of","title":"Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias","date":"2025-03-05","arxiv_id":"2503.03595","repositories_listed":0,"syntology":null},{"url":null,"slug":"volume-tells-dual-cycle-consistent-diffusion","title":"Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution","date":"2025-03-04","arxiv_id":"2503.02261","repositories_listed":0,"syntology":null},{"url":null,"slug":"accord-alleviating-concept-coupling-through","title":"ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization","date":"2025-03-03","arxiv_id":"2503.01122","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-functional-maps-diffusion-models","title":"Denoising Functional Maps: Diffusion Models for Shape Correspondence","date":"2025-03-03","arxiv_id":"2503.01845","repositories_listed":0,"syntology":null},{"url":null,"slug":"frmd-fast-robot-motion-diffusion-with","title":"FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation","date":"2025-03-03","arxiv_id":"2503.02048","repositories_listed":0,"syntology":null},{"url":null,"slug":"handrawer-leveraging-spatial-information-to","title":"HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage","date":"2025-03-03","arxiv_id":"2503.02127","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-infrared-image-deblurring-and-event","title":"Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging","date":"2025-03-03","arxiv_id":"2503.01193","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-graph-neural-networks-with-1","title":"Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery","date":"2025-03-03","arxiv_id":"2503.01227","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-bivariate-signals-via-smoothing-and","title":"Denoising bivariate signals via smoothing and polarization priors","date":"2025-02-28","arxiv_id":"2502.20827","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffbrush-just-painting-the-art-by-your-hands","title":"DiffBrush:Just Painting the Art by Your Hands","date":"2025-02-28","arxiv_id":"2502.20904","repositories_listed":0,"syntology":null},{"url":null,"slug":"pet-image-denoising-via-text-guided-diffusion","title":"PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts","date":"2025-02-28","arxiv_id":"2502.21260","repositories_listed":0,"syntology":null},{"url":null,"slug":"bevdiffuser-plug-and-play-diffusion-model-for","title":"BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance","date":"2025-02-27","arxiv_id":"2502.19694","repositories_listed":0,"syntology":null},{"url":null,"slug":"cmim-a-contrastive-mutual-information","title":"cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning","date":"2025-02-27","arxiv_id":"2502.19642","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexidit-your-diffusion-transformer-can","title":"FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute","date":"2025-02-27","arxiv_id":"2502.20126","repositories_listed":0,"syntology":null}],"record_sha256":"28e76148c635a75f6b1ca32586313f86276a99ee19043438ef07104fd3705517","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}