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 22 / 27 页

1051. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
1052. ICML 2023Calibrating Multimodal Learning
1053. ICML 2023CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
1054. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
1055. ICML 2023Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
1056. ICML 2023CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
1057. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
1058. ICML 2023Composer: Creative and Controllable Image Synthesis with Composable Conditions
1059. ICML 2023Compositional Exemplars for In-context Learning
1060. ICML 2023Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
1061. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
1062. ICML 2023ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
1063. ICML 2023Conditionally Strongly Log-Concave Generative Models
1064. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
1065. ICML 2023Consistency Models
1066. ICML 2023Continuation Path Learning for Homotopy Optimization
1067. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
1068. ICML 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
1069. ICML 2023Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
1070. ICML 2023Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
1071. ICML 2023Counterfactual Identifiability of Bijective Causal Models
1072. ICML 2023Coupled Variational Autoencoder
1073. ICML 2023Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
1074. ICML 2023Data-Copying in Generative Models: A Formal Framework
1075. ICML 2023DDGR: Continual Learning with Deep Diffusion-based Generative Replay
1076. ICML 2023Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
1077. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
1078. ICML 2023Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
1079. ICML 2023Deep Graph Representation Learning and Optimization for Influence Maximization
1080. ICML 2023Deep linear networks can benignly overfit when shallow ones do
1081. ICML 2023Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
1082. ICML 2023Denoising MCMC for Accelerating Diffusion-Based Generative Models
1083. ICML 2023DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
1084. ICML 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
1085. ICML 2023Deterministic equivalent and error universality of deep random features learning
1086. ICML 2023Differentiable and Transportable Structure Learning
1087. ICML 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
1088. ICML 2023Diffusion Based Representation Learning
1089. ICML 2023Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
1090. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
1091. ICML 2023Directed Chain Generative Adversarial Networks
1092. ICML 2023Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models
1093. ICML 2023Disentangled Generative Models for Robust Prediction of System Dynamics
1094. ICML 2023Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
1095. ICML 2023Distributed Linear Bandits under Communication Constraints
1096. ICML 2023Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
1097. ICML 2023Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
1098. ICML 2023Do Machine Learning Models Learn Statistical Rules Inferred from Data?
1099. ICML 2023Doubly Optimal No-Regret Learning in Monotone Games
1100. ICML 2023Dropout Reduces Underfitting

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。