扩散模型论文趋势 2023–2026
扩散、flow matching 与 score-based 生成
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 6,900 篇被归入该主题的论文。
主要结论
- 扩散模型论文从 2023 年的 660 篇增长到 2026 年的 2,691 篇，三年间扩大到约 4.1 倍。
- NeurIPS 是发扩散模型论文最多的会议，2023–2026 年共 2,202 篇，占四个会议总量的 32%。
- 交叉方向里涨得最快的是应用 · chemistry physics and earth sciences：同时属于两个主题的论文从 2023 年的 14 篇增至 2026 年的 163 篇。
- 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 1；同组增长最快的深度学习 · foundation models是 2.5 倍，扩散模型是 4.1 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 327 | 86 | 109 | 138 | 660 |
| 2024 | 520 | 260 | 195 | 496 | 1,471 |
| 2025 | 694 | 487 | 346 | 551 | 2,078 |
| 2026 | 661 | 694 | 728 | 608 | 2,691 |
代表论文
按年份和会议均匀挑选，方便快速看到每一届的样貌
- NeurIPS 2026$\mathbf{D^{3}S^{2}}$: Diffusion-Guided Dataset Distillation for Semantic Segmentation
- ICML 2026“Do Diffusion Models Dream of Electric Planes?” Discrete and Continuous Simulation-Based Inference for Aircraft Design
- ICLR 2026$\alpha$-DPO: Robust Preference Alignment for Diffusion Models via $\alpha$ Divergence
- CVPR 20262ndMatch Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching
- NeurIPS 2025$\text{S}^2$Q-VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation
- ICML 2025A First-order Generative Bilevel Optimization Framework for Diffusion Models
- ICLR 2025$\text{I}^2\text{AM}$: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps
- CVPR 20253DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
- NeurIPS 2024$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation
- ICML 2024A Dense Reward View on Aligning Text-to-Image Diffusion with Preference
- ICLR 2024$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
- CVPR 2024D^4: Dataset Distillation via Disentangled Diffusion Model
- NeurIPS 20233D molecule generation by denoising voxel grids
- ICML 2023A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification
- ICLR 2023(Certified!!) Adversarial Robustness for Free!
- CVPR 20233D Neural Field Generation Using Triplane Diffusion
- NeurIPS 2026$\text{PartConcepts}$: A Unified Mechanism for Fine-Grained Part Localization and Generation
- ICML 2026$\sigma$: Sigmoid Modulation for Ultra High Resolution Diffusion
- ICLR 2026$\textit{MADFormer}$: Mixed Autoregressive and Diffusion Transformers for Continuous Image Generation
- CVPR 20263D-LATTE Latent Space 3D Editing from Textual Instructions
全部扩散模型论文
共 694 篇，第 10 / 14 页
- NeurIPS 2025PocketSR: The Super-Resolution Expert in Your Pocket Mobiles
- NeurIPS 2025PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
- NeurIPS 2025Point-MaDi: Masked Autoencoding with Diffusion for Point Cloud Pre-training
- NeurIPS 2025PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors
- NeurIPS 2025PoseCrafter: Extreme Pose Estimation with Hybrid Video Synthesis
- NeurIPS 2025Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics
- NeurIPS 2025Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism
- NeurIPS 2025Precise Diffusion Inversion: Towards Novel Samples and Few-Step Models
- NeurIPS 2025Preconditioned Langevin Dynamics with Score-based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
- NeurIPS 2025Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
- NeurIPS 2025Preference Learning with Response Time: Robust Losses and Guarantees
- NeurIPS 2025Preference-Guided Diffusion for Multi-Objective Offline Optimization
- NeurIPS 2025Principled Long-Tailed Generative Modeling via Diffusion Models
- NeurIPS 2025Prior-Guided Diffusion Planning for Offline Reinforcement Learning
- NeurIPS 2025Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
- NeurIPS 2025Pro3D-Editor: A Progressive Framework for Consistent and Precise 3D Editing
- NeurIPS 2025Probing Equivariance and Symmetry Breaking in Convolutional Networks
- NeurIPS 2025Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
- NeurIPS 2025Projection-Manifold Regularized Latent Diffusion for Robust General Image Fusion
- NeurIPS 2025Promptable 3-D Object Localization with Latent Diffusion Models
- NeurIPS 2025Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
- NeurIPS 2025Quantifying Cross-Modality Memorization in Vision-Language Models
- NeurIPS 2025Radial Attention: $\mathcal O(n \log n)$ Sparse Attention for Long Video Generation
- NeurIPS 2025Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
- NeurIPS 2025Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
- NeurIPS 2025Rare Text Semantics Were Always There in Your Diffusion Transformer
- NeurIPS 2025Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
- NeurIPS 2025Real-Time Execution of Action Chunking Flow Policies
- NeurIPS 2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
- NeurIPS 2025Reconstruct, Inpaint, Test-Time Finetune: Dynamic Novel-view Synthesis from Monocular Videos
- NeurIPS 2025Rectified CFG++ for Flow Based Models
- NeurIPS 2025ReDi: Rectified Discrete Flow
- NeurIPS 2025REDOUBT: Duo Safety Validation for Autonomous Vehicle Motion Planning
- NeurIPS 2025ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning
- NeurIPS 2025Reinforced Context Order Recovery for Adaptive Reasoning and Planning
- NeurIPS 2025Reinforcement Learning Meets Masked Generative Models: Mask-GRPO for Text-to-Image Generation
- NeurIPS 2025Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models
- NeurIPS 2025RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
- NeurIPS 2025Remasking Discrete Diffusion Models with Inference-Time Scaling
- NeurIPS 2025Removing Concepts from Text-to-Image Models with Only Negative Samples
- NeurIPS 2025REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training
- NeurIPS 2025RepLDM: Reprogramming Pretrained Latent Diffusion Models for High-Quality, High-Efficiency, High-Resolution Image Generation
- NeurIPS 2025Representation Entanglement for Generation: Training Diffusion Transformers Is Much Easier Than You Think
- NeurIPS 2025Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design
- NeurIPS 2025Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues
- NeurIPS 2025RespoDiff: Dual-Module Bottleneck Transformation for Responsible & Faithful T2I Generation
- NeurIPS 2025Rethinking Losses for Diffusion Bridge Samplers
- NeurIPS 2025RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
- NeurIPS 2025Reverse Diffusion Sequential Monte Carlo Samplers
- NeurIPS 2025Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查扩散模型在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "diffusion-models"}
常见问题
- 2023–2026 年四大会议一共有多少篇扩散模型论文？
- 共 6,900 篇，按年份分别是 2023 年 660 篇、2024 年 1,471 篇、2025 年 2,078 篇、2026 年 2,691 篇。
- NeurIPS 2026 有多少篇扩散模型论文？
- NeurIPS 2026 有 661 篇扩散模型论文，上一届是 694 篇。
- ICLR 2026 有多少篇扩散模型论文？
- ICLR 2026 有 694 篇扩散模型论文，上一届是 487 篇。
- ICML 2026 有多少篇扩散模型论文？
- ICML 2026 有 728 篇扩散模型论文，上一届是 346 篇。
- CVPR 2026 有多少篇扩散模型论文？
- CVPR 2026 有 608 篇扩散模型论文，上一届是 551 篇。
- 哪个会议的扩散模型论文最多？
- NeurIPS，2023–2026 年共 2,202 篇。
「深度学习」下的其他主题
- 深度学习 · generative models and autoencoders 4,314
- 深度学习 · large language models 2,334
- 深度学习 · self supervised learning 1,958
- 深度学习 · graph neural networks 1,648
- 深度学习 · optimization 953
- 深度学习 · representation learning 802
- 深度学习 · probabilistic methods 637
- 深度学习 · foundation models 536
- 深度学习 · causal inference 316
- 深度学习 · transfer learning 197