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 76 篇，第 2 / 2 页

51. CVPR 2023Overcoming the Trade-Off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
52. CVPR 2023Persistent Nature: A Generative Model of Unbounded 3D Worlds
53. CVPR 2023PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
54. CVPR 2023Reliable and Interpretable Personalized Federated Learning
55. CVPR 2023Restoration of Hand-Drawn Architectural Drawings Using Latent Space Mapping With Degradation Generator
56. CVPR 2023Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens
57. CVPR 2023Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution
58. CVPR 2023RGBD2: Generative Scene Synthesis via Incremental View Inpainting Using RGBD Diffusion Models
59. CVPR 2023RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
60. CVPR 2023SceneTrilogy: On Human Scene-Sketch and Its Complementarity With Photo and Text
61. CVPR 2023Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
62. CVPR 2023Seeing a Rose in Five Thousand Ways
63. CVPR 2023ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations
64. CVPR 2023SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
65. CVPR 2023SMAE: Few-Shot Learning for HDR Deghosting With Saturation-Aware Masked Autoencoders
66. CVPR 2023Spider GAN: Leveraging Friendly Neighbors To Accelerate GAN Training
67. CVPR 2023The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
68. CVPR 2023Towards End-to-End Generative Modeling of Long Videos With Memory-Efficient Bidirectional Transformers
69. CVPR 2023Towards Practical Plug-and-Play Diffusion Models
70. CVPR 2023Towards Transferable Targeted Adversarial Examples
71. CVPR 2023Towards Universal Fake Image Detectors That Generalize Across Generative Models
72. CVPR 2023Understanding Deep Generative Models With Generalized Empirical Likelihoods
73. CVPR 2023Understanding Masked Autoencoders via Hierarchical Latent Variable Models
74. CVPR 2023VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking
75. CVPR 2023Visual Prompt Tuning for Generative Transfer Learning
76. CVPR 2023Zero-Shot Model Diagnosis

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。