{"total":111,"rows":[{"id":"2410.00083","title":"A Survey on Diffusion Models for Inverse Problems","created":"2024-09-30","cited_by_count":180,"area":"diffusion"},{"id":"2407.21720","title":"Detecting, Explaining, and Mitigating Memorization in Diffusion Models","created":"2024-07-31","cited_by_count":95,"area":"diffusion"},{"id":"2405.13712","title":"Learning Diffusion Priors from Observations by Expectation Maximization","created":"2024-05-22","cited_by_count":61,"area":"diffusion"},{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60,"area":"diffusion"},{"id":"2403.11052","title":"Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention","created":"2024-03-17","cited_by_count":58,"area":"diffusion"},{"id":"2406.10209","title":"Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs","created":"2024-06-14","cited_by_count":50,"area":null},{"id":"2404.10177","title":"Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data","created":"2024-03-20","cited_by_count":44,"area":"diffusion"},{"id":"2307.00467","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values","created":"2023-07-02","cited_by_count":39,"area":"diffusion"},{"id":"2401.00909","title":"Taming Mode Collapse in Score Distillation for Text-to-3D Generation","created":"2023-12-31","cited_by_count":36,"area":null},{"id":"2403.08728","title":"Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data","created":"2024-03-13","cited_by_count":32,"area":"diffusion"},{"id":"2508.17689","title":"On the Edge of Memorization in Diffusion Models","created":"2025-08-25","cited_by_count":22,"area":"diffusion"},{"id":"2407.10641","title":"Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems","created":"2024-07-15","cited_by_count":21,"area":"diffusion"},{"id":"2410.16152","title":"Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models","created":"2024-10-21","cited_by_count":21,"area":"diffusion"},{"id":"2407.01014","title":"An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations","created":"2024-07-01","cited_by_count":20,"area":"diffusion"},{"id":"2410.21665","title":"Exploring Local Memorization in Diffusion Models via Bright Ending Attention","created":"2024-10-29","cited_by_count":19,"area":"diffusion"},{"id":"2411.02780","title":"How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion","created":"2024-11-05","cited_by_count":18,"area":null},{"id":"2506.10038","title":"Ambient Diffusion Omni: Training Good Models with Bad Data","created":"2025-06-10","cited_by_count":18,"area":"diffusion"},{"id":"2502.03435","title":"Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization","created":"2025-02-05","cited_by_count":17,"area":"diffusion"},{"id":"2502.21278","title":"Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion","created":"2025-02-28","cited_by_count":17,"area":"diffusion"},{"id":"2508.01975","title":"Diffusion models for inverse problems","created":"2025-08-04","cited_by_count":16,"area":"diffusion"},{"id":"2308.14409","title":"Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction","created":"2023-08-28","cited_by_count":15,"area":"diffusion"},{"id":"2401.09031","title":"Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation","created":"2024-01-17","cited_by_count":14,"area":"diffusion"},{"id":"2403.14837","title":"Osmosis: RGBD Diffusion Prior for Underwater Image Restoration","created":"2024-03-21","cited_by_count":13,"area":"diffusion"},{"id":"2412.04648","title":"Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise","created":"2024-12-05","cited_by_count":13,"area":null},{"id":"2407.11162","title":"Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images","created":"2024-07-15","cited_by_count":12,"area":"diffusion"},{"id":"2407.15328","title":"Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models","created":"2024-07-22","cited_by_count":12,"area":"diffusion"},{"id":"2309.04856","title":"AmbientFlow: Invertible generative models from incomplete, noisy measurements","created":"2023-09-09","cited_by_count":11,"area":null},{"id":"2405.17955","title":"Efficient Prior Calibration From Indirect Data","created":"2024-05-28","cited_by_count":11,"area":null},{"id":"2408.00001","title":"Replication in Visual Diffusion Models: A Survey and Outlook","created":"2024-07-07","cited_by_count":11,"area":"diffusion"},{"id":"2502.05446","title":"Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets","created":"2025-02-08","cited_by_count":11,"area":"diffusion"},{"id":"2401.11115","title":"MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation","created":"2024-01-20","cited_by_count":10,"area":"diffusion"},{"id":"2503.15557","title":"Motion Synthesis with Sparse and Flexible Keyjoint Control","created":"2025-03-18","cited_by_count":9,"area":null},{"id":"2505.11853","title":"Measurement Score-Based Diffusion Model","created":"2025-05-17","cited_by_count":8,"area":"diffusion"},{"id":"2506.00557","title":"Score Matching With Missing Data","created":"2025-05-31","cited_by_count":8,"area":null},{"id":"2503.07578","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","created":"2025-03-10","cited_by_count":7,"area":"diffusion"},{"id":"2308.02958","title":"K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets","created":"2023-08-05","cited_by_count":6,"area":null},{"id":"2407.17907","title":"Amortized Posterior Sampling with Diffusion Prior Distillation","created":"2024-07-25","cited_by_count":6,"area":"diffusion"},{"id":"2504.12112","title":"A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction","created":"2025-04-16","cited_by_count":6,"area":null},{"id":"2505.03154","title":"StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data","created":"2025-05-06","cited_by_count":6,"area":null},{"id":"2509.25705","title":"How Diffusion Models Memorize","created":"2025-09-30","cited_by_count":6,"area":"diffusion"},{"id":"2510.12691","title":"DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization","created":"2025-10-14","cited_by_count":6,"area":"diffusion"},{"id":"2511.04671","title":"X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations","created":"2025-11-06","cited_by_count":6,"area":"diffusion"},{"id":"2512.10857","title":"Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants","created":"2025-12-11","cited_by_count":6,"area":null},{"id":"2403.07842","title":"DP-TLDM: Differentially Private Tabular Latent Diffusion Model","created":"2024-03-12","cited_by_count":5,"area":"diffusion"},{"id":"2403.09327","title":"Perspective-Equivariance for Unsupervised Imaging with Camera Geometry","created":"2024-03-14","cited_by_count":5,"area":null},{"id":"2407.17095","title":"MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models","created":"2024-07-24","cited_by_count":5,"area":"diffusion"},{"id":"2410.21669","title":"Investigating Memorization in Video Diffusion Models","created":"2024-10-29","cited_by_count":5,"area":"diffusion"},{"id":"2502.11333","title":"Inverse Flow and Consistency Models","created":"2025-02-17","cited_by_count":5,"area":"diffusion"},{"id":"2506.13763","title":"Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value","created":"2025-06-16","cited_by_count":5,"area":"diffusion"},{"id":"2601.03244","title":"Self-Supervised Learning from Noisy and Incomplete Data","created":"2026-01-06","cited_by_count":5,"area":null},{"id":"2411.12919","title":"Robust multi-coil MRI reconstruction via self-supervised denoising","created":"2024-11-19","cited_by_count":4,"area":null},{"id":"2502.14009","title":"Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction","created":"2025-02-19","cited_by_count":4,"area":null},{"id":"2307.16422","title":"Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution","created":"2023-07-31","cited_by_count":3,"area":null},{"id":"2408.16852","title":"The Star Geometry of Critic-Based Regularizer Learning","created":"2024-08-29","cited_by_count":3,"area":null},{"id":"2409.07025","title":"CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion","created":"2024-09-11","cited_by_count":3,"area":"diffusion"},{"id":"2410.20255","title":"Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation","created":"2024-10-26","cited_by_count":3,"area":"diffusion"},{"id":"2501.03430","title":"A Self-supervised Diffusion Bridge for MRI Reconstruction","created":"2025-01-06","cited_by_count":3,"area":"diffusion"},{"id":"2502.07645","title":"From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback","created":"2025-02-11","cited_by_count":3,"area":"offlinerl"},{"id":"2504.00264","title":"DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models","created":"2025-03-31","cited_by_count":3,"area":"diffusion"},{"id":"2505.22841","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","created":"2025-05-28","cited_by_count":3,"area":"diffusion"},{"id":"2509.20098","title":"Incomplete Data, Complete Dynamics: A Diffusion Approach","created":"2025-09-24","cited_by_count":3,"area":null},{"id":"2510.11839","title":"WaveletDiff: Multilevel Wavelet Diffusion For Time Series Generation","created":"2025-10-13","cited_by_count":3,"area":null},{"id":"2510.11964","title":"Normalization-equivariant Diffusion Models: Learning Posterior Samplers From Noisy And Partial Measurements","created":"2025-10-13","cited_by_count":3,"area":"diffusion"},{"id":"2410.11241","title":"Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method","created":"2024-10-15","cited_by_count":2,"area":"diffusion"},{"id":"2512.17051","title":"SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples","created":"2025-12-18","cited_by_count":2,"area":null},{"id":"2601.15417","title":"Ambient Dataloops: Generative Models for Dataset Refinement","created":"2026-01-21","cited_by_count":2,"area":"diffusion"},{"id":"2602.13873","title":"Ambient Physics: Training Neural PDE Solvers with Partial Observations","created":"2026-02-14","cited_by_count":2,"area":null},{"id":"2604.09436","title":"SCoRe: Clean Image Generation from Diffusion Models Trained on Noisy Images","created":"2026-04-10","cited_by_count":2,"area":"diffusion"},{"id":"2605.13852","title":"Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning","created":"2026-03-25","cited_by_count":2,"area":null},{"id":"2310.07887","title":"Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise","created":"2023-10-11","cited_by_count":1,"area":null},{"id":"2402.02081","title":"Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples","created":"2024-02-03","cited_by_count":1,"area":"diffusion"},{"id":"2405.19931","title":"Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks","created":"2024-05-30","cited_by_count":1,"area":"diffusion"},{"id":"2503.21501","title":"Double Blind Imaging with Generative Modeling","created":"2025-03-27","cited_by_count":1,"area":null},{"id":"2506.02371","title":"SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples","created":"2025-06-03","cited_by_count":1,"area":"diffusion"},{"id":"2510.00929","title":"Equivariant Splitting: Self-supervised learning from incomplete data","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2510.06145","title":"Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2512.17493","title":"UPMRI: Unsupervised Parallel MRI Reconstruction via Projected Conditional Flow Matching","created":"2025-12-19","cited_by_count":1,"area":"diffusion"},{"id":"2601.18728","title":"Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data","created":"2026-01-26","cited_by_count":1,"area":null},{"id":"2604.21066","title":"Optimizing Diffusion Priors in Image Reconstruction from a Single Observation","created":"2026-04-22","cited_by_count":1,"area":"diffusion"},{"id":"2605.14341","title":"AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors","created":"2026-05-14","cited_by_count":1,"area":"diffusion"},{"id":"2606.01865","title":"Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections","created":"2026-06-01","cited_by_count":1,"area":"diffusion"},{"id":"2412.00176","title":"Opt-In Art: Learning Art Styles Only from Few Examples","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2502.09434","title":"Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models","created":"2025-02-13","cited_by_count":0,"area":"diffusion"},{"id":"2505.11825","title":"Bootstrapping Diffusion: Diffusion Model Training Leveraging Partial and Corrupted Data","created":"2025-05-17","cited_by_count":0,"area":"diffusion"},{"id":"2505.21742","title":"What is Adversarial Training for Diffusion Models?","created":"2025-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2509.18402","title":"Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation","created":"2025-09-22","cited_by_count":0,"area":"diffusion"},{"id":"2509.24710","title":"MAD: Manifold Attracted Diffusion","created":"2025-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2510.26097","title":"Robust Super-Capacity SRS Channel Inpainting via Diffusion Models","created":"2025-10-30","cited_by_count":0,"area":"diffusion"},{"id":"2511.11435","title":"The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models","created":"2025-11-14","cited_by_count":0,"area":"diffusion"},{"id":"2512.11194","title":"Beyond Memorization: Selective Learning for Copyright-Safe Diffusion Model Training","created":"2025-12-12","cited_by_count":0,"area":"diffusion"},{"id":"2512.14187","title":"Establishing Stochastic Object Models from Noisy Data via Ambient Measurement-Integrated Diffusion","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2512.24847","title":"AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference","created":"2025-12-31","cited_by_count":0,"area":null},{"id":"2601.22029","title":"Inverse Problems Conditioned on Observation Ensembles: Applications and Methods","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.22279","title":"Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2603.14074","title":"Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.00307","title":"SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2605.02908","title":"Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings","created":"2026-04-06","cited_by_count":0,"area":"diffusion"},{"id":"2605.11506","title":"Principled Design of Diffusion-based Optimizers for Inverse Problems","created":"2026-05-12","cited_by_count":0,"area":"diffusion"},{"id":"2605.14326","title":"D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.16818","title":"Observation-Aligned Mask Priors for Learning Physical Fields from Authentic Occlusions","created":"2026-05-16","cited_by_count":0,"area":null}],"next_cursor":"100"}