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 2 / 27 页

51. ICML 2026Biased Generalization in Diffusion Models
52. ICML 2026Boosting World Models Learning via Latent-Space Value Alignment
53. ICML 2026Breaking the Factorization Barrier in Diffusion Language Models
54. ICML 2026Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
55. ICML 2026Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
56. ICML 2026Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning
57. ICML 2026BRIDGE: Predicting Human Task Completion Time From Model Performance
58. ICML 2026Bridging Functional and Representational Similarity via Usable Information
59. ICML 2026Budget-Constrained Step-Level Diffusion Caching
60. ICML 2026C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
61. ICML 2026CacheEdit: Efficient Multi-round Image Editing via Adaptive Token-wise Reuse.
62. ICML 2026Calibrated Test-Time Guidance for Bayesian Inference
63. ICML 2026Calibrating Generative Models to Distributional Constraints
64. ICML 2026CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping
65. ICML 2026CamGeo: Sparse Camera-Conditioned Image-to-Video Generation with 3D Geometry Priors
66. ICML 2026CANDI: Hybrid Discrete-Continuous Diffusion Models
67. ICML 2026Capability Traps in DPO
68. ICML 2026CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
69. ICML 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features
70. ICML 2026Categorical Flow Maps
71. ICML 2026Categorical Reparameterization with Denoising Diffusion Models
72. ICML 2026CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching
73. ICML 2026Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
74. ICML 2026Causal Representation Learning with Optimal Compression and Complex Treatments
75. ICML 2026Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
76. ICML 2026CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
77. ICML 2026Co-Generative De Novo Functional Protein Design
78. ICML 2026CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation
79. ICML 2026CoFrGeNet: Continued Fraction Architectures for Language Generation
80. ICML 2026CoGenCast: A Coupled Autoregressive–Flow Generative Framework for Time Series Forecasting
81. ICML 2026CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
82. ICML 2026ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
83. ICML 2026Compositional Generative Modeling from Decentralized Data
84. ICML 2026Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
85. ICML 2026Computational Arbitrage in AI Model Markets
86. ICML 2026Compute Where it Counts: Self Optimizing Language Models
87. ICML 2026Concept Removal for Frontier Image Generative Models
88. ICML 2026Concept-Guided Tokenization: Closing the Gap Between Reconstruction and Generation
89. ICML 2026Condition-Aware Graph Flow Matching for Modeling the Distributions of Complex Fluid Systems
90. ICML 2026Conf-Gen: Conformal Uncertainty Quantification for Generative Models
91. ICML 2026Confidence is Not Universal: Task-Dependent Calibration and Emergent Behavior in LLMs
92. ICML 2026Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
93. ICML 2026Conformal C2ST: Turning weak classifiers into strong two-sample tests
94. ICML 2026Consistent Diffusion Language Models
95. ICML 2026Content-Style Identification via Differential Independence
96. ICML 2026Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
97. ICML 2026Contrastive Flow Map Matching
98. ICML 2026ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
99. ICML 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
100. ICML 2026CountsDiff: A diffusion model on the natural numbers for generation and imputation of count-based data

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。