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 1,430 篇，第 22 / 29 页

1051. ICML 2026Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture – Bridging Predictive and Generative Self-Supervised Learning
1052. ICML 2026Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
1053. ICML 2026Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
1054. ICML 2026Variational Bayesian Flow Network for Graph Generation
1055. ICML 2026Variational Entropic Optimal Transport
1056. ICML 2026Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
1057. ICML 2026VecDesigner: Exploring Visual Guidance and Structural Consistency for Semantic Typography
1058. ICML 2026VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
1059. ICML 2026VELR: Efficient Video Reward Feedback via Ensemble Latent Reward Models
1060. ICML 2026VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization
1061. ICML 2026VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
1062. ICML 2026VideoMAETok: Boosting Video Diffusion Models via Masked Autoencoders as Tokenizers
1063. ICML 2026Visual Implicit Autoregressive Modeling
1064. ICML 2026ViTok-v2: Scaling Native Resolution Autoencoders to 5 Billion Parameters
1065. ICML 2026Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
1066. ICML 2026Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
1067. ICML 2026When Diffusion Language Models Hesitate: Detecting and Correcting Visual Hallucinations via Confidence Fluctuation
1068. ICML 2026When Do Diffusion Models Learn to Generate Multiple Objects?
1069. ICML 2026When Model Merging Breaks Routing: Training-Free Calibration for MoE
1070. ICML 2026When RL Meets Adaptive Speculative Training: A Unified Training-Serving System
1071. ICML 2026When Sample Selection Bias Precipitates Model Collapse
1072. ICML 2026Where Concept Erasure Should Occur: Concept–Layer Alignment in Text-to-Video Diffusion Models
1073. ICML 2026Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
1074. ICML 2026Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
1075. ICML 2026WhisperSplat: Lossless Steganography in 3D Gaussian Splatting
1076. ICML 2026Who Said Neural Networks Aren't Linear?
1077. ICML 2026Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics
1078. ICML 2026Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
1079. ICML 2026XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding
1080. ICML 2026You Can Learn Tokenization End-to-End with Reinforcement Learning
1081. ICML 2026You Don’t Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
1082. NeurIPS 2026$\mathcal{P}$Torch: Narrowing the Gap Between Projection and Gradient-Based Learning
1083. NeurIPS 2026A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs
1084. NeurIPS 2026A Locally Tokenized Generative Model for Robust Time-Series Watermarking
1085. NeurIPS 2026A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
1086. NeurIPS 2026A simple model of co-emergence of grid and place fields
1087. NeurIPS 2026A Unifying Perspective on Language Model Interpretability
1088. NeurIPS 2026ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
1089. NeurIPS 2026Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
1090. NeurIPS 2026Active Learning for Conditional Generative Compressed Sensing
1091. NeurIPS 2026Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel
1092. NeurIPS 2026Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
1093. NeurIPS 2026AegisFlow: Training-free Non-myopic Path-safe Guided Flow Matching
1094. NeurIPS 2026AesGI-Bench: Benchmarking and Evaluating the Aesthetic Quality of AI-Generated Images via Large Multimodal Models
1095. NeurIPS 2026AI-Assisted Classification under Correlation Neglect and Trust
1096. NeurIPS 2026Align $\&$ Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
1097. NeurIPS 2026Align as You Couple: Learning Spatial Resolved Inference from H&E Images with Mollified Flow Matching
1098. NeurIPS 2026Aligning Flow Map Policies with Optimal $Q$-Guidance
1099. NeurIPS 2026All-Addition Spiking Diffusion Models with Attention Enhancement
1100. NeurIPS 2026AMGenC: Generating Charge Balanced Amorphous Materials

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。