深度学习 · generative models and autoencoders论文趋势 2023–2026

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。

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 275 篇，第 3 / 6 页

101. ICML 2025Generative Data Mining with Longtail-Guided Diffusion
102. ICML 2025Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion
103. ICML 2025Generative Intervention Models for Causal Perturbation Modeling
104. ICML 2025Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning
105. ICML 2025Generative Point Cloud Registration
106. ICML 2025GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
107. ICML 2025Geometric Generative Modeling with Noise-Conditioned Graph Networks
108. ICML 2025Geometric Representation Condition Improves Equivariant Molecule Generation
109. ICML 2025Geometry Informed Tokenization of Molecules for Language Model Generation
110. ICML 2025Geometry-Informed Neural Networks
111. ICML 2025GMAIL: Generative Modality Alignment for generated Image Learning
112. ICML 2025Graph Generative Pre-trained Transformer
113. ICML 2025GraphGPT: Generative Pre-trained Graph Eulerian Transformer
114. ICML 2025Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres
115. ICML 2025HashAttention: Semantic Sparsity for Faster Inference
116. ICML 2025Hessian Geometry of Latent Space in Generative Models
117. ICML 2025Highly Compressed Tokenizer Can Generate Without Training
118. ICML 2025How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
119. ICML 2025How to Evaluate and Mitigate IP Infringement in Visual Generative AI?
120. ICML 2025How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
121. ICML 2025Hyper-Transforming Latent Diffusion Models
122. ICML 2025I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
123. ICML 2025Identifiable Object Representations under Spatial Ambiguities
124. ICML 2025IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
125. ICML 2025Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme
126. ICML 2025Improving Consistency Models with Generator-Augmented Flows
127. ICML 2025Improving Flow Matching by Aligning Flow Divergence
128. ICML 2025Improving the Diffusability of Autoencoders
129. ICML 2025Improving the Scaling Laws of Synthetic Data with Deliberate Practice
130. ICML 2025IMTS is Worth Time $\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction
131. ICML 2025Inductive Moment Matching
132. ICML 2025Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
133. ICML 2025InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
134. ICML 2025INRFlow: Flow Matching for INRs in Ambient Space
135. ICML 2025Interpreting CLIP with Hierarchical Sparse Autoencoders
136. ICML 2025Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion
137. ICML 2025Inverse Bridge Matching Distillation
138. ICML 2025Inverse Flow and Consistency Models
139. ICML 2025Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
140. ICML 2025Inverse problems with experiment-guided AlphaFold
141. ICML 2025IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
142. ICML 2025Is Noise Conditioning Necessary for Denoising Generative Models?
143. ICML 2025Kinetic Langevin Diffusion for Crystalline Materials Generation
144. ICML 2025LapSum - One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection
145. ICML 2025Latent Thought Models with Variational Bayes Inference-Time Computation
146. ICML 2025Learning Extrapolative Sequence Transformations from Markov Chains
147. ICML 2025Learning Latent Graph Structures and their Uncertainty
148. ICML 2025Learning Monotonic Probabilities with a Generative Cost Model
149. ICML 2025Learning Multi-Level Features with Matryoshka Sparse Autoencoders
150. ICML 2025Learning Robust Neural Processes with Risk-Averse Stochastic Optimization

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。