深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向：generative_models_and_autoencoders
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 4,314 篇被归入该主题的论文。
主要结论
- 深度学习 · generative models and autoencoders论文从 2023 年的 1,216 篇增长到 2026 年的 1,430 篇，三年间扩大到约 1.2 倍。
- NeurIPS 是发深度学习 · generative models and autoencoders论文最多的会议，2023–2026 年共 1,714 篇，占四个会议总量的 40%。
- 交叉方向里涨得最快的是图像生成：同时属于两个主题的论文从 2023 年的 35 篇增至 2026 年的 150 篇。
- 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 2；同组增长最快的扩散模型是 4.1 倍，深度学习 · generative models and autoencoders是 1.2 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
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 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
- ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
- ICLR 2026``Noisier'’ Noise Contrastive Estimation is (Almost) Maximum Likelihood
- CVPR 2026Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
- NeurIPS 2025Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
- ICML 2025A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
- ICLR 2025A Watermark for Order-Agnostic Language Models
- CVPR 2025DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
- NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
- ICML 2024A Fixed-Point Approach for Causal Generative Modeling
- ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
- CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
- NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
- ICML 2023A Kernelized Stein Discrepancy for Biological Sequences
- ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
- CVPR 20233D GAN Inversion With Facial Symmetry Prior
- NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
- ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
- ICLR 2026Analyzing and Evaluating Unbiased Language Model Watermark
- CVPR 2026Bidirectional Normalizing Flow From Data to Noise and Back
全部深度学习 · generative models and autoencoders论文
共 900 篇，第 11 / 18 页
- ICML 2025Hessian Geometry of Latent Space in Generative Models
- ICML 2025Highly Compressed Tokenizer Can Generate Without Training
- ICML 2025How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
- ICML 2025How to Evaluate and Mitigate IP Infringement in Visual Generative AI?
- ICML 2025How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
- ICML 2025Hyper-Transforming Latent Diffusion Models
- ICML 2025I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
- ICML 2025Identifiable Object Representations under Spatial Ambiguities
- ICML 2025IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
- ICML 2025Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme
- ICML 2025Improving Consistency Models with Generator-Augmented Flows
- ICML 2025Improving Flow Matching by Aligning Flow Divergence
- ICML 2025Improving the Diffusability of Autoencoders
- ICML 2025Improving the Scaling Laws of Synthetic Data with Deliberate Practice
- ICML 2025IMTS is Worth Time $\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
- ICML 2025Inductive Moment Matching
- ICML 2025Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
- ICML 2025InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
- ICML 2025INRFlow: Flow Matching for INRs in Ambient Space
- ICML 2025Interpreting CLIP with Hierarchical Sparse Autoencoders
- ICML 2025Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion
- ICML 2025Inverse Bridge Matching Distillation
- ICML 2025Inverse Flow and Consistency Models
- ICML 2025Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
- ICML 2025Inverse problems with experiment-guided AlphaFold
- ICML 2025IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
- ICML 2025Is Noise Conditioning Necessary for Denoising Generative Models?
- ICML 2025Kinetic Langevin Diffusion for Crystalline Materials Generation
- ICML 2025LapSum - One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection
- ICML 2025Latent Thought Models with Variational Bayes Inference-Time Computation
- ICML 2025Learning Extrapolative Sequence Transformations from Markov Chains
- ICML 2025Learning Latent Graph Structures and their Uncertainty
- ICML 2025Learning Monotonic Probabilities with a Generative Cost Model
- ICML 2025Learning Multi-Level Features with Matryoshka Sparse Autoencoders
- ICML 2025Learning Robust Neural Processes with Risk-Averse Stochastic Optimization
- ICML 2025Learning to Match Unpaired Data with Minimum Entropy Coupling
- ICML 2025Learning to Quantize for Training Vector-Quantized Networks
- ICML 2025Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
- ICML 2025LGDM: Latent Guidance in Diffusion Models for Perceptual Evaluations
- ICML 2025LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data
- ICML 2025Low-Rank Adapting Models for Sparse Autoencoders
- ICML 2025Masked Autoencoders Are Effective Tokenizers for Diffusion Models
- ICML 2025Masked Generative Nested Transformers with Decode Time Scaling
- ICML 2025MDDM: Practical Message-Driven Generative Image Steganography Based on Diffusion Models
- ICML 2025Measuring Diversity: Axioms and Challenges
- ICML 2025Mechanisms of Projective Composition of Diffusion Models
- ICML 2025MGD$^3$ : Mode-Guided Dataset Distillation using Diffusion Models
- ICML 2025MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
- ICML 2025MissScore: High-Order Score Estimation in the Presence of Missing Data
- ICML 2025MITIGATING OVER-EXPLORATION IN LATENT SPACE OPTIMIZATION USING LES
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 查深度学习 · generative models and autoencoders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": "dl-generative-models-and-autoencoders"}
常见问题
- 2023–2026 年四大会议一共有多少篇深度学习 · generative models and autoencoders论文？
- 共 4,314 篇，按年份分别是 2023 年 1,216 篇、2024 年 768 篇、2025 年 900 篇、2026 年 1,430 篇。
- NeurIPS 2026 有多少篇深度学习 · generative models and autoencoders论文？
- NeurIPS 2026 有 349 篇深度学习 · generative models and autoencoders论文，上一届是 240 篇。
- ICLR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICLR 2026 有 303 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- ICML 2026 有多少篇深度学习 · generative models and autoencoders论文？
- ICML 2026 有 604 篇深度学习 · generative models and autoencoders论文，上一届是 275 篇。
- CVPR 2026 有多少篇深度学习 · generative models and autoencoders论文？
- CVPR 2026 有 174 篇深度学习 · generative models and autoencoders论文，上一届是 110 篇。
- 哪个会议的深度学习 · generative models and autoencoders论文最多？
- NeurIPS，2023–2026 年共 1,714 篇。