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 1,304 篇，第 23 / 27 页

1101. ICML 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
1102. ICML 2023DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
1103. ICML 2023DUET: 2D Structured and Approximately Equivariant Representations
1104. ICML 2023Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
1105. ICML 2023Effective Structured Prompting by Meta-Learning and Representative Verbalizer
1106. ICML 2023Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
1107. ICML 2023Entropy-driven Unsupervised Keypoint Representation Learning in Videos
1108. ICML 2023Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
1109. ICML 2023Evidential Interactive Learning for Medical Image Captioning
1110. ICML 2023Evolving Semantic Prototype Improves Generative Zero-Shot Learning
1111. ICML 2023Exact Inference in High-order Structured Prediction
1112. ICML 2023Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule
1113. ICML 2023Expected Gradients of Maxout Networks and Consequences to Parameter Initialization
1114. ICML 2023Explainable Data-Driven Optimization: From Context to Decision and Back Again
1115. ICML 2023Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
1116. ICML 2023Fair and Optimal Classification via Post-Processing
1117. ICML 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
1118. ICML 2023FARE: Provably Fair Representation Learning with Practical Certificates
1119. ICML 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
1120. ICML 2023FeDXL: Provable Federated Learning for Deep X-Risk Optimization
1121. ICML 2023Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
1122. ICML 2023Few-Sample Feature Selection via Feature Manifold Learning
1123. ICML 2023Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
1124. ICML 2023FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
1125. ICML 2023Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
1126. ICML 2023FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
1127. ICML 2023FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
1128. ICML 2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
1129. ICML 2023From Perception to Programs: Regularize, Overparameterize, and Amortize
1130. ICML 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
1131. ICML 2023Fully Dynamic Submodular Maximization over Matroids
1132. ICML 2023Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
1133. ICML 2023Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
1134. ICML 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
1135. ICML 2023Generative Adversarial Symmetry Discovery
1136. ICML 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
1137. ICML 2023Generative Decoding of Visual Stimuli
1138. ICML 2023Generative Graph Dictionary Learning
1139. ICML 2023Generative Pretraining for Black-Box Optimization
1140. ICML 2023Geometric Autoencoders - What You See is What You Decode
1141. ICML 2023GFlowOut: Dropout with Generative Flow Networks
1142. ICML 2023Go Beyond Imagination: Maximizing Episodic Reachability with World Models
1143. ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
1144. ICML 2023Graphically Structured Diffusion Models
1145. ICML 2023Grounding Language Models to Images for Multimodal Inputs and Outputs
1146. ICML 2023Guiding Pretraining in Reinforcement Learning with Large Language Models
1147. ICML 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
1148. ICML 2023Hierarchical Diffusion for Offline Decision Making
1149. ICML 2023Hierarchical Imitation Learning with Vector Quantized Models
1150. ICML 2023High Fidelity Image Counterfactuals with Probabilistic Causal Models

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。