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 2,690 篇，第 44 / 54 页
- NeurIPS 2026DeFlowCritic: Dense Latent Reward Alignment for Text-to-Image Flow Matching Models
- NeurIPS 2026Denoising Time Matters: Diverse Generation in Diffusion Language Models
- NeurIPS 2026Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
- NeurIPS 2026Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
- NeurIPS 2026DexOPE: 6D Object Pose Estimation in Dexterous Manipulation
- NeurIPS 2026Diff-Aid: Inference-time Adaptive Interaction Denoising for Rectified Text-to-Image Generation
- NeurIPS 2026DiffATS: Diffusion in Aligned Tensor Space
- NeurIPS 2026DiffCool: Label-Free Synthesis of Chip-Tailored Heat Sinks via Thermal-Aware Diffusion
- NeurIPS 2026Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
- NeurIPS 2026Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models
- NeurIPS 2026DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting
- NeurIPS 2026DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing
- NeurIPS 2026DiffRisk: Diffusion Representation Learning with Informative Missingness for Health Risk Prediction
- NeurIPS 2026Diffusion fine-tuning with Rewarded Moment Matching Distillation
- NeurIPS 2026Diffusion Language Models Can Approximate Optimal Infilling Lengths Implicitly
- NeurIPS 2026Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation
- NeurIPS 2026Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
- NeurIPS 2026Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors
- NeurIPS 2026Diffusion Models without Classifier-free Guidance
- NeurIPS 2026Diffusion Path Samplers via Sequential Monte Carlo
- NeurIPS 2026Diffusion Transformers with Residual Adaptive Layer Normalization
- NeurIPS 2026Diffusion-State Policy Optimization for Masked Diffusion Language Models
- NeurIPS 2026Diffusion-Time Concept Manifolds: Sparse Autoencoder Groups for Interpreting Denoising Language Models
- NeurIPS 2026Diffusion-warm sampling of the XY model enables fast thermalization at scale
- NeurIPS 2026DiLaDiff: Distilled Latent-augmented Diffusion for Language Modeling
- NeurIPS 2026DiPhon: Diffusion on Graphons for Scalable Graph Generation
- NeurIPS 2026Direct Conditioning of Audio Diffusion Transformers on fMRI Reveals Cortical Contributions to Sound Reconstruction
- NeurIPS 2026Direct Product Flow Matching: Decoupling Radial and Angular Dynamics for Few-Shot Adaptation
- NeurIPS 2026DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
- NeurIPS 2026Directional Noise Conditioning for Diffusion Models
- NeurIPS 2026DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
- NeurIPS 2026Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks
- NeurIPS 2026Discrete Diffusion Playground: A 2D Benchmark for Discrete Generative Models
- NeurIPS 2026Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
- NeurIPS 2026Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
- NeurIPS 2026Disen-Forcing: Disentangling Semantic Anchoring from Motion for Autoregressive Video Diffusion
- NeurIPS 2026Disentangled Representation Learning via Flow Matching
- NeurIPS 2026Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
- NeurIPS 2026Disentangling Dual Image References in Frequency Aware Diffusion Models for Personalized Generation
- NeurIPS 2026DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
- NeurIPS 2026Do Diffusion Models Learn to Generalize Basic Visual Skills?
- NeurIPS 2026Don't Match the Noise: Distribution Matching under Unknown Measurement Error with an Audit Sample
- NeurIPS 2026Drift-React: One-step Generation of Reaction Pathways via SE(3) Drifting Fields
- NeurIPS 2026Drifting Field Policy: Wasserstein Gradient Flow on Policy Space for Offline-to-Online RL
- NeurIPS 2026Drifting Fields are not Conservative
- NeurIPS 2026DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
- NeurIPS 2026DSAQuant: Denoising-Stage-Aligned Quantization-Aware Training for Video Generation
- NeurIPS 2026DSSA: Dynamic Sparse Semantic Anchoring for Purifying Protective Perturbations against Diffusion Models
- NeurIPS 2026Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
- NeurIPS 2026DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling
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