{"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/31","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":31,"pages_in_order":73,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/task/denoising/papers/32","papers":[{"url":null,"slug":"toward-theoretical-insights-into-diffusion","title":"Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging","date":"2025-05-21","arxiv_id":"2505.16024","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-cyclic-diffusion-for-inference","title":"Adaptive Cyclic Diffusion for Inference Scaling","date":"2025-05-20","arxiv_id":"2505.14036","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-diffusion-denoising","title":"Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism","date":"2025-05-20","arxiv_id":"2505.14741","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-noise-robustness-of-llm-based-zero","title":"Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising","date":"2025-05-20","arxiv_id":"2505.13830","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-swin-transformer-framework-for","title":"Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction","date":"2025-05-20","arxiv_id":"2505.14897","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-inverse-scattering-with-score-based","title":"Neural Inverse Scattering with Score-based Regularization","date":"2025-05-20","arxiv_id":"2505.14560","repositories_listed":0,"syntology":null},{"url":null,"slug":"refidiff-refinement-aware-diffusion-for","title":"RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation","date":"2025-05-20","arxiv_id":"2505.14451","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-similarity-score-functions-to","title":"Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs","date":"2025-05-20","arxiv_id":"2505.14739","repositories_listed":0,"syntology":null},{"url":null,"slug":"anti-inpainting-a-proactive-defense-against","title":"Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions","date":"2025-05-19","arxiv_id":"2505.13023","repositories_listed":0,"syntology":null},{"url":null,"slug":"restoration-score-distillation-from-corrupted","title":"Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation","date":"2025-05-19","arxiv_id":"2505.13377","repositories_listed":0,"syntology":null},{"url":null,"slug":"ropecraft-training-free-motion-transfer-with","title":"RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers","date":"2025-05-19","arxiv_id":"2505.13344","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-orthogonal-regularization-for-deep","title":"Stochastic Orthogonal Regularization for deep projective priors","date":"2025-05-19","arxiv_id":"2505.13078","repositories_listed":0,"syntology":null},{"url":null,"slug":"ctlformer-a-hybrid-denoising-model-combining","title":"CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction","date":"2025-05-18","arxiv_id":"2505.12203","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-concept-unlearning-with-low-rank","title":"Few-Shot Concept Unlearning with Low Rank Adaptation","date":"2025-05-18","arxiv_id":"2505.12395","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-diffusion-based-super-resolution","title":"Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling","date":"2025-05-17","arxiv_id":"2505.12048","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-mutual-knowledge-distillation-in-bi","title":"Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning","date":"2025-05-17","arxiv_id":"2505.12074","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10881","title":"Prior-Guided Diffusion Planning for Offline Reinforcement Learning","date":"2025-05-16","arxiv_id":"2505.10881","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10999","title":"DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning","date":"2025-05-16","arxiv_id":"2505.10999","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11037","title":"Evolutionary training-free guidance in diffusion model for 3D multi-objective molecular generation","date":"2025-05-16","arxiv_id":"2505.11037","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11158","title":"Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review","date":"2025-05-16","arxiv_id":"2505.11158","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11232","title":"AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition","date":"2025-05-16","arxiv_id":"2505.11232","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11267","title":"Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity","date":"2025-05-16","arxiv_id":"2505.11267","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11278","title":"A Fourier Space Perspective on Diffusion Models","date":"2025-05-16","arxiv_id":"2505.11278","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11306","title":"Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion","date":"2025-05-16","arxiv_id":"2505.11306","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11471","title":"CRISP: Clustering Multi-Vector Representations for Denoising and Pruning","date":"2025-05-16","arxiv_id":"2505.11471","repositories_listed":0,"syntology":null},{"url":"/paper/attend-to-not-attended-structure-then-detail","slug":"attend-to-not-attended-structure-then-detail","title":"Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration","date":"2025-05-16","arxiv_id":"2505.11707","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/attend-to-not-attended-structure-then-detail#ran","syntology_url":"https://syntology.ai/paper/2505.11707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.11707"}},"official":null}},{"url":null,"slug":"bandrc-band-shifted-raised-cosine-activated","title":"BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations","date":"2025-05-16","arxiv_id":"2505.11640","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-diffusion-policies-with","title":"Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps","date":"2025-05-15","arxiv_id":"2505.10482","repositories_listed":0,"syntology":null},{"url":null,"slug":"orl-ldm-offline-reinforcement-learning-guided","title":"ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction","date":"2025-05-15","arxiv_id":"2505.10027","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-circuit-completeness-in-language","title":"Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates","date":"2025-05-15","arxiv_id":"2505.10039","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-pixels-leveraging-the-language-of","title":"Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos","date":"2025-05-14","arxiv_id":"2505.09455","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-and-alignment-rethinking-domain","title":"Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing","date":"2025-05-14","arxiv_id":"2505.09484","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-recommender-models-and-the-illusion","title":"Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch","date":"2025-05-14","arxiv_id":"2505.09364","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-full-field-evolution-of-physical","title":"Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations","date":"2025-05-14","arxiv_id":"2505.09284","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-knowledge-graph-embedding-via","title":"Robust Knowledge Graph Embedding via Denoising","date":"2025-05-14","arxiv_id":"2505.18171","repositories_listed":0,"syntology":null},{"url":null,"slug":"transdiffuser-end-to-end-trajectory","title":"TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving","date":"2025-05-14","arxiv_id":"2505.09315","repositories_listed":0,"syntology":null},{"url":null,"slug":"behind-the-noise-conformal-quantile","title":"Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations","date":"2025-05-13","arxiv_id":"2505.08176","repositories_listed":0,"syntology":null},{"url":null,"slug":"condisim-conditional-diffusion-models-for","title":"ConDiSim: Conditional Diffusion Models for Simulation Based Inference","date":"2025-05-13","arxiv_id":"2505.08403","repositories_listed":0,"syntology":null},{"url":null,"slug":"eventdiff-a-unified-and-efficient-diffusion","title":"EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation","date":"2025-05-13","arxiv_id":"2505.08235","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-cocoercive-conservative-denoisers","title":"Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems","date":"2025-05-13","arxiv_id":"2505.08909","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-autonomous-uav-visual-object-search","title":"Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology","date":"2025-05-13","arxiv_id":"2505.08765","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-fingerprint-construction-for-massive","title":"Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach","date":"2025-05-12","arxiv_id":"2505.07893","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsemext-unlocking-the-potential-of-sparse","title":"SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction","date":"2025-05-12","arxiv_id":"2505.08808","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-for-icra-2025-goose-2d","title":"Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding","date":"2025-05-11","arxiv_id":"2505.06991","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-guidance-controlling-the-outputs-of","title":"Topology Guidance: Controlling the Outputs of Generative Models via Vector Field Topology","date":"2025-05-11","arxiv_id":"2505.06804","repositories_listed":0,"syntology":null},{"url":null,"slug":"burger-robust-graph-denoising-augmentation","title":"Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation","date":"2025-05-10","arxiv_id":"2505.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"profashion-prototype-guided-fashion-video","title":"ProFashion: Prototype-guided Fashion Video Generation with Multiple Reference Images","date":"2025-05-10","arxiv_id":"2505.06537","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-tensor-singular-value-thresholding-a-non","title":"Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising","date":"2025-05-09","arxiv_id":"2505.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-learning-of-semantic-embedding","title":"Automated Learning of Semantic Embedding Representations for Diffusion Models","date":"2025-05-09","arxiv_id":"2505.05732","repositories_listed":0,"syntology":null},{"url":null,"slug":"computationally-efficient-diffusion-models-in","title":"Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review","date":"2025-05-09","arxiv_id":"2505.07866","repositories_listed":0,"syntology":null},{"url":null,"slug":"insertion-language-models-sequence-generation","title":"Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions","date":"2025-05-09","arxiv_id":"2505.05755","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-diffusion-probabilistic-models-for-8","title":"Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting","date":"2025-05-08","arxiv_id":"2505.05381","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusionsfm-predicting-structure-and-motion","title":"DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion","date":"2025-05-08","arxiv_id":"2505.05473","repositories_listed":0,"syntology":null},{"url":null,"slug":"edmamba-a-simple-yet-effective-event","title":"EDmamba: A Simple yet Effective Event Denoising Method with State Space Model","date":"2025-05-08","arxiv_id":"2505.05391","repositories_listed":0,"syntology":null},{"url":null,"slug":"graffe-graph-representation-learning-via","title":"Graffe: Graph Representation Learning via Diffusion Probabilistic Models","date":"2025-05-08","arxiv_id":"2505.04956","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-diffusion-generation-model-of-speakers","title":"Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication","date":"2025-05-08","arxiv_id":"2505.04996","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdaa-diff-ct-guided-multi-dose-adaptive","title":"MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising","date":"2025-05-08","arxiv_id":"2505.05112","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-based-self-supervised-mri-denoising","title":"Score-based Self-supervised MRI Denoising","date":"2025-05-08","arxiv_id":"2505.05631","repositories_listed":0,"syntology":null},{"url":null,"slug":"countdiffusion-text-to-image-synthesis-with","title":"CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion","date":"2025-05-07","arxiv_id":"2505.04347","repositories_listed":0,"syntology":null},{"url":null,"slug":"femsn-frequency-enhanced-multiscale-network","title":"FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments","date":"2025-05-07","arxiv_id":"2505.06285","repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-denoising-diffusion-probabilistic","title":"Riemannian Denoising Diffusion Probabilistic Models","date":"2025-05-07","arxiv_id":"2505.04338","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-and-temporal-denoising-for","title":"Spectral and Temporal Denoising for Differentially Private Optimization","date":"2025-05-07","arxiv_id":"2505.04468","repositories_listed":0,"syntology":null},{"url":null,"slug":"coop-wd-cooperative-perception-with-weighting","title":"Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication","date":"2025-05-06","arxiv_id":"2505.03528","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-models-are-secretly-exchangeable","title":"Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation","date":"2025-05-06","arxiv_id":"2505.03983","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-glass-defect-detection-with","title":"Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control","date":"2025-05-06","arxiv_id":"2505.03134","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexiact-towards-flexible-action-control-in","title":"FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios","date":"2025-05-06","arxiv_id":"2505.03730","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-for-speech-denoising","title":"Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance","date":"2025-05-06","arxiv_id":"2505.03442","repositories_listed":0,"syntology":null},{"url":null,"slug":"mri-motion-correction-via-efficient-residual","title":"MRI motion correction via efficient residual-guided denoising diffusion probabilistic models","date":"2025-05-06","arxiv_id":"2505.03498","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-convergence-of-score-based","title":"Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients","date":"2025-05-06","arxiv_id":"2505.03432","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-prompting-for-diverse-count-level-pet","title":"Dual Prompting for Diverse Count-level PET Denoising","date":"2025-05-05","arxiv_id":"2505.03037","repositories_listed":0,"syntology":null},{"url":null,"slug":"dualreal-adaptive-joint-training-for-lossless","title":"DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization","date":"2025-05-04","arxiv_id":"2505.02192","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantizing-diffusion-models-from-a-sampling","title":"Quantizing Diffusion Models from a Sampling-Aware Perspective","date":"2025-05-04","arxiv_id":"2505.02242","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-black-litterman-portfolio-via","title":"Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction","date":"2025-05-03","arxiv_id":"2505.01781","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-preserving-image-denoising-via-multi","title":"Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing","date":"2025-05-02","arxiv_id":"2505.01032","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-time-series-data-augmentation-model-through","title":"A Time-Series Data Augmentation Model through Diffusion and Transformer Integration","date":"2025-05-01","arxiv_id":"2505.03790","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-driven-segmentation-and-analysis-of","title":"AI-Driven Segmentation and Analysis of Microbial Cells","date":"2025-05-01","arxiv_id":"2505.00578","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-diffusion-model-surrogates-for","title":"Generative diffusion model surrogates for mechanistic agent-based biological models","date":"2025-05-01","arxiv_id":"2505.09630","repositories_listed":0,"syntology":null},{"url":null,"slug":"quaternion-wavelet-conditioned-diffusion","title":"Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution","date":"2025-05-01","arxiv_id":"2505.00334","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-critical-traffic-simulation-with-1","title":"Safety-Critical Traffic Simulation with Guided Latent Diffusion Model","date":"2025-05-01","arxiv_id":"2505.00515","repositories_listed":0,"syntology":null},{"url":null,"slug":"garmentdiffusion-3d-garment-sewing-pattern","title":"GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers","date":"2025-04-30","arxiv_id":"2504.21476","repositories_listed":0,"syntology":null},{"url":null,"slug":"adept-annotation-denoising-auxiliary-tasks","title":"Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining","date":"2025-04-29","arxiv_id":"2504.20800","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusionrir-room-impulse-response","title":"DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models","date":"2025-04-29","arxiv_id":"2504.20625","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-for-physical-layer-1","title":"Generative AI for Physical-Layer Authentication","date":"2025-04-25","arxiv_id":"2504.18175","repositories_listed":0,"syntology":null},{"url":null,"slug":"hepatogen-generating-hepatobiliary-phase-mri","title":"HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models","date":"2025-04-25","arxiv_id":"2504.18405","repositories_listed":0,"syntology":null},{"url":null,"slug":"outlier-aware-tensor-robust-principal","title":"Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation","date":"2025-04-25","arxiv_id":"2504.18323","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-approach-for-denoising-and","title":"A Machine Learning Approach for Denoising and Upsampling HRTFs","date":"2025-04-24","arxiv_id":"2504.17586","repositories_listed":0,"syntology":null},{"url":null,"slug":"ckmdiff-a-generative-diffusion-model-for-ckm","title":"CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors","date":"2025-04-24","arxiv_id":"2504.17323","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolution-meets-diffusion-efficient-neural","title":"Evolution Meets Diffusion: Efficient Neural Architecture Generation","date":"2025-04-24","arxiv_id":"2504.17827","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-autoregressive-models-for-continuous","title":"Fast Autoregressive Models for Continuous Latent Generation","date":"2025-04-24","arxiv_id":"2504.18391","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-noise-adaptive-mri-denoising","title":"Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning","date":"2025-04-24","arxiv_id":"2504.17698","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-probabilistic-models-for","title":"Diffusion Probabilistic Models for Compressive SAR Imaging","date":"2025-04-23","arxiv_id":"2504.17053","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecgdedrdnet-a-deep-learning-based-method-for","title":"ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network","date":"2025-04-23","arxiv_id":"2505.05477","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerial-active-star-ris-assisted-satellite","title":"Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications","date":"2025-04-22","arxiv_id":"2504.16146","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-controlled-diffusion-for-denoising-in","title":"Self-Controlled Diffusion for Denoising in Scientific Imaging","date":"2025-04-22","arxiv_id":"2504.16951","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-learning-method-for-raman","title":"A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders","date":"2025-04-21","arxiv_id":"2504.16130","repositories_listed":0,"syntology":null},{"url":null,"slug":"dyst-xl-dynamic-layout-planning-and-content","title":"DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation","date":"2025-04-21","arxiv_id":"2504.15032","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-primal-dual-splitting-for-self","title":"Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction","date":"2025-04-21","arxiv_id":"2504.15390","repositories_listed":0,"syntology":null},{"url":null,"slug":"flowloss-dynamic-flow-conditioned-loss","title":"FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models","date":"2025-04-20","arxiv_id":"2504.14535","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-artificial-intelligence-enabled-signature","title":"An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio","date":"2025-04-19","arxiv_id":"2504.14226","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-emulation-of-chaotic-dynamics-with","title":"Generative emulation of chaotic dynamics with coherent prior","date":"2025-04-19","arxiv_id":"2504.14264","repositories_listed":0,"syntology":null}],"record_sha256":"5aa093189d7b3d543332e8b7d7e6ddd60a8d94109b6a69b09fdac6d8febba7d9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}