{"total":100,"rows":[{"id":"2006.09011","title":"Improved Techniques for Training Score-Based Generative Models","created":"2020-06-16","cited_by_count":1043,"area":"diffusion"},{"id":"2112.07804","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","created":"2021-12-15","cited_by_count":516,"area":"diffusion"},{"id":"2301.11445","title":"3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models","created":"2023-01-26","cited_by_count":458,"area":"diffusion"},{"id":"2211.16677","title":"3D Neural Field Generation using Triplane Diffusion","created":"2022-11-30","cited_by_count":274,"area":"diffusion"},{"id":"2306.04675","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","created":"2023-06-07","cited_by_count":205,"area":"diffusion"},{"id":"2302.06588","title":"Raising the Cost of Malicious AI-Powered Image Editing","created":"2023-02-13","cited_by_count":163,"area":"diffusion"},{"id":"2210.05475","title":"GENIE: Higher-Order Denoising Diffusion Solvers","created":"2022-10-11","cited_by_count":127,"area":"diffusion"},{"id":"2302.13861","title":"Differentially Private Diffusion Models Generate Useful Synthetic Images","created":"2023-02-27","cited_by_count":86,"area":"diffusion"},{"id":"2203.17260","title":"Generating High Fidelity Data from Low-density Regions using Diffusion Models","created":"2022-03-31","cited_by_count":78,"area":"diffusion"},{"id":"2405.05967","title":"Distilling Diffusion Models into Conditional GANs","created":"2024-05-09","cited_by_count":78,"area":"diffusion"},{"id":"2208.13753","title":"Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis","created":"2022-08-29","cited_by_count":75,"area":"diffusion"},{"id":"2111.12701","title":"Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes","created":"2021-11-24","cited_by_count":71,"area":"diffusion"},{"id":"2401.05779","title":"Erasing Undesirable Influence in Diffusion Models","created":"2024-01-11","cited_by_count":57,"area":"diffusion"},{"id":"2305.00562","title":"Class-Balancing Diffusion Models","created":"2023-04-30","cited_by_count":48,"area":"diffusion"},{"id":"2306.01902","title":"Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation","created":"2023-06-02","cited_by_count":37,"area":"diffusion"},{"id":"2304.01565","title":"A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material","created":"2023-04-04","cited_by_count":35,"area":"diffusion"},{"id":"2303.00165","title":"Diffusion Probabilistic Fields","created":"2023-03-01","cited_by_count":28,"area":"diffusion"},{"id":"2403.17422","title":"InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion","created":"2024-03-26","cited_by_count":28,"area":"diffusion"},{"id":"2305.19638","title":"A Unified Framework for U-Net Design and Analysis","created":"2023-05-31","cited_by_count":27,"area":"diffusion"},{"id":"2310.19427","title":"Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans","created":"2023-10-30","cited_by_count":27,"area":"diffusion"},{"id":"2302.02913","title":"Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design","created":"2023-02-06","cited_by_count":23,"area":"diffusion"},{"id":"2502.20946","title":"Generative Uncertainty in Diffusion Models","created":"2025-02-28","cited_by_count":23,"area":"diffusion"},{"id":"2301.13622","title":"Learning Data Representations with Joint Diffusion Models","created":"2023-01-31","cited_by_count":22,"area":"diffusion"},{"id":"2306.04098","title":"Phoenix: A Federated Generative Diffusion Model","created":"2023-06-07","cited_by_count":22,"area":"diffusion"},{"id":"2501.04765","title":"TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training","created":"2025-01-08","cited_by_count":21,"area":"diffusion"},{"id":"2411.04873","title":"Boosting Latent Diffusion with Perceptual Objectives","created":"2024-11-06","cited_by_count":20,"area":"diffusion"},{"id":"2112.09788","title":"Heavy-tailed denoising score matching","created":"2021-12-17","cited_by_count":18,"area":"diffusion"},{"id":"2401.08815","title":"Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive","created":"2024-01-16","cited_by_count":18,"area":"diffusion"},{"id":"2404.18928","title":"Stylus: Automatic Adapter Selection for Diffusion Models","created":"2024-04-29","cited_by_count":18,"area":"diffusion"},{"id":"2407.18609","title":"Heavy-Tailed Diffusion with Denoising L\\'evy Probabilistic Models","created":"2024-07-26","cited_by_count":18,"area":"diffusion"},{"id":"2402.17517","title":"Label-Noise Robust Diffusion Models","created":"2024-02-27","cited_by_count":16,"area":"diffusion"},{"id":"2306.12438","title":"Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback","created":"2023-06-16","cited_by_count":15,"area":"diffusion"},{"id":"2406.10808","title":"Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching","created":"2024-06-16","cited_by_count":13,"area":"diffusion"},{"id":"2409.06493","title":"Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models","created":"2024-09-09","cited_by_count":13,"area":"diffusion"},{"id":"2408.16266","title":"Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification","created":"2024-08-29","cited_by_count":11,"area":"diffusion"},{"id":"2301.07969","title":"Fast Inference in Denoising Diffusion Models via MMD Finetuning","created":"2023-01-19","cited_by_count":9,"area":"diffusion"},{"id":"2506.15864","title":"Improving Rectified Flow with Boundary Conditions","created":"2025-06-18","cited_by_count":9,"area":"diffusion"},{"id":"2602.09651","title":"The Entropic Signature of Class Speciation in Diffusion Models","created":"2026-02-10","cited_by_count":9,"area":"diffusion"},{"id":"2311.13570","title":"WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space","created":"2023-11-22","cited_by_count":8,"area":"diffusion"},{"id":"2502.16972","title":"SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories","created":"2025-02-24","cited_by_count":7,"area":"diffusion"},{"id":"2311.03303","title":"TS-Diffusion: Generating Highly Complex Time Series with Diffusion Models","created":"2023-11-06","cited_by_count":6,"area":"diffusion"},{"id":"2402.10821","title":"Training Class-Imbalanced Diffusion Model Via Overlap Optimization","created":"2024-02-16","cited_by_count":6,"area":"diffusion"},{"id":"2504.07392","title":"ID-Booth: Identity-consistent Face Generation with Diffusion Models","created":"2025-04-10","cited_by_count":6,"area":"diffusion"},{"id":"2510.13080","title":"Counting Hallucinations in Diffusion Models","created":"2025-10-15","cited_by_count":6,"area":"diffusion"},{"id":"2404.05666","title":"YaART: Yet Another ART Rendering Technology","created":"2024-04-08","cited_by_count":5,"area":"diffusion"},{"id":"2405.19201","title":"Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations","created":"2024-05-29","cited_by_count":5,"area":"diffusion"},{"id":"2503.06558","title":"Generative modelling with jump-diffusions","created":"2025-03-09","cited_by_count":5,"area":"diffusion"},{"id":"2504.02545","title":"MAD: Makeup All-in-One with Cross-Domain Diffusion Model","created":"2025-04-03","cited_by_count":5,"area":"diffusion"},{"id":"2506.22463","title":"Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization","created":"2025-06-18","cited_by_count":5,"area":"diffusion"},{"id":"2309.07277","title":"Limitations of Face Image Generation","created":"2023-09-13","cited_by_count":4,"area":"diffusion"},{"id":"2310.17261","title":"Attribute Based Interpretable Evaluation Metrics for Generative Models","created":"2023-10-26","cited_by_count":4,"area":"diffusion"},{"id":"2402.16369","title":"Generative AI in Vision: A Survey on Models, Metrics and Applications","created":"2024-02-26","cited_by_count":4,"area":"diffusion"},{"id":"2406.11713","title":"Latent Denoising Diffusion GAN: Faster sampling, Higher image quality","created":"2024-06-17","cited_by_count":4,"area":"diffusion"},{"id":"2601.22816","title":"Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features","created":"2026-01-30","cited_by_count":4,"area":"diffusion"},{"id":"2302.10688","title":"On Calibrating Diffusion Probabilistic Models","created":"2023-02-21","cited_by_count":3,"area":"diffusion"},{"id":"2312.09792","title":"Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology","created":"2023-12-15","cited_by_count":3,"area":"diffusion"},{"id":"2408.06995","title":"Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models","created":"2024-08-13","cited_by_count":3,"area":"diffusion"},{"id":"2408.14584","title":"DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning","created":"2024-08-26","cited_by_count":3,"area":"diffusion"},{"id":"2411.12523","title":"Data Pruning in Generative Diffusion Models","created":"2024-11-19","cited_by_count":3,"area":"diffusion"},{"id":"2503.11544","title":"AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition","created":"2025-03-14","cited_by_count":3,"area":"diffusion"},{"id":"2507.02687","title":"APT: Adaptive Personalized Training for Diffusion Models with Limited Data","created":"2025-07-03","cited_by_count":3,"area":"diffusion"},{"id":"2607.15485","title":"Diffusion models recover accurate mixture weights despite score function insensitivity","created":"2026-07-16","cited_by_count":3,"area":"diffusion"},{"id":"2308.16847","title":"Diffusion Models for Interferometric Satellite Aperture Radar","created":"2023-08-31","cited_by_count":2,"area":"diffusion"},{"id":"2403.13916","title":"Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques","created":"2024-03-20","cited_by_count":2,"area":"diffusion"},{"id":"2506.08796","title":"Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling","created":"2025-06-10","cited_by_count":2,"area":"diffusion"},{"id":"2509.15357","title":"MaskAttn-SDXL: Controllable Region-Level Text-To-Image Generation","created":"2025-09-18","cited_by_count":2,"area":"diffusion"},{"id":"2211.10747","title":"Exploring validation metrics for offline model-based optimisation with diffusion models","created":"2022-11-19","cited_by_count":1,"area":"diffusion"},{"id":"2305.14674","title":"T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities","created":"2023-05-24","cited_by_count":1,"area":"diffusion"},{"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":"2501.13915","title":"Binary Diffusion Probabilistic Model","created":"2025-01-23","cited_by_count":1,"area":"diffusion"},{"id":"2506.11378","title":"The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis","created":"2025-06-13","cited_by_count":1,"area":"diffusion"},{"id":"2507.11441","title":"Implementing Adaptations for Vision AutoRegressive Model","created":"2025-07-15","cited_by_count":1,"area":"diffusion"},{"id":"2508.07926","title":"Score Augmentation for Diffusion Models","created":"2025-08-11","cited_by_count":1,"area":"diffusion"},{"id":"2508.14871","title":"Squeezed Diffusion Models","created":"2025-08-20","cited_by_count":1,"area":"diffusion"},{"id":"2511.11074","title":"Evaluating Latent Generative Paradigms for High-Fidelity 3D Shape Completion from a Single Depth Image","created":"2025-11-14","cited_by_count":1,"area":"diffusion"},{"id":"2605.06153","title":"Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles","created":"2026-05-07","cited_by_count":1,"area":"diffusion"},{"id":"2608.00024","title":"Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance","created":"2026-07-10","cited_by_count":1,"area":"diffusion"},{"id":"2608.03082","title":"DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers","created":"2026-08-04","cited_by_count":1,"area":"diffusion"},{"id":"2608.20818","title":"Scaling Muon for Diffusion Transformers","created":"2026-08-21","cited_by_count":1,"area":"diffusion"},{"id":"2307.13720","title":"Composite Diffusion | whole >= \\Sigma parts","created":"2023-07-25","cited_by_count":0,"area":"diffusion"},{"id":"2309.17123","title":"Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining","created":"2023-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2403.16790","title":"I-Diff: Structural Regularization for High-Fidelity Diffusion Models","created":"2024-03-25","cited_by_count":0,"area":"diffusion"},{"id":"2407.21159","title":"Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models","created":"2024-07-30","cited_by_count":0,"area":"diffusion"},{"id":"2505.04992","title":"Boosting Statistic Learning with Synthetic Data from Pretrained Large Models","created":"2025-05-08","cited_by_count":0,"area":"diffusion"},{"id":"2506.05960","title":"AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models","created":"2025-06-06","cited_by_count":0,"area":"diffusion"},{"id":"2506.15381","title":"When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class","created":"2025-06-18","cited_by_count":0,"area":"diffusion"},{"id":"2508.08146","title":"An effective potential for generative modelling with active matter","created":"2025-08-11","cited_by_count":0,"area":"diffusion"},{"id":"2508.09598","title":"Images Speak Louder Than Scores: Failure Mode Escape for Enhancing Generative Quality","created":"2025-08-13","cited_by_count":0,"area":"diffusion"},{"id":"2511.21415","title":"DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models","created":"2025-11-26","cited_by_count":0,"area":"diffusion"},{"id":"2601.03443","title":"Discriminating real and synthetic super-resolved audio samples using embedding-based classifiers","created":"2026-01-06","cited_by_count":0,"area":"diffusion"},{"id":"2602.00384","title":"RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints","created":"2026-01-30","cited_by_count":0,"area":"diffusion"},{"id":"2605.11773","title":"Is Monotonic Sampling Necessary in Diffusion Models?","created":"2026-05-12","cited_by_count":0,"area":"diffusion"},{"id":"2605.12951","title":"Coreset-Induced Conditional Velocity Flow Matching","created":"2026-05-13","cited_by_count":0,"area":"diffusion"},{"id":"2606.27696","title":"Class-frequency Guided Noise Schedule for Diffusion Models","created":"2026-06-26","cited_by_count":0,"area":"diffusion"},{"id":"2607.18516","title":"Signed Rectified Flow: Negativity-Controlled Generation","created":"2026-07-20","cited_by_count":0,"area":"diffusion"},{"id":"2607.19126","title":"Parallel Noising in Neural Markov Logic Networks","created":"2026-07-21","cited_by_count":0,"area":"diffusion"},{"id":"2608.03990","title":"Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation","created":"2026-08-04","cited_by_count":0,"area":"diffusion"},{"id":"2609.36210","title":"On the spectral properties of generative denoiser Jacobians","created":"2026-09-28","cited_by_count":0,"area":"diffusion"},{"id":"2610.01408","title":"Smoother Flow Matching via Contrastive Trajectory Repulsion","created":"2026-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.10430","title":"Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields","created":"2026-10-07","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}