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 1 / 27 页

1. ICML 2026(1D) Ordered Tokens Enable Efficient Test-Time Search
2. ICML 2026$f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
3. ICML 20263D MeanFlow: One-Step Point Cloud Completion and Generation via Average-Velocity Transport
4. ICML 2026A Diffusive Classification Loss for Learning Energy-based Generative Models
5. ICML 2026A Flat Vocabulary or a Rich Hierarchy? Re-introducing Intrinsic Structure Transforms the Autoregressive Image Generation
6. ICML 2026A Kinetic Energy Perspective of Flow Matching
7. ICML 2026A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies
8. ICML 2026A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
9. ICML 2026A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization
10. ICML 2026A Theoretical Framework for Statistical Evaluability of Generative Models
11. ICML 2026A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
12. ICML 2026A Unifying View of Variational Generative Wasserstein Flows
13. ICML 2026AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
14. ICML 2026Adapting Noise to Data: Generative Flows from Learned 1D Processes
15. ICML 2026Adversarial Flow Models
16. ICML 2026Adversarial Latent Embedding Repair for LLM Continual Learning
17. ICML 2026Align Your Trajectory Tangent: Training Better Consistency Models via Manifold-Aligned Tangents
18. ICML 2026Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models
19. ICML 2026Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training
20. ICML 2026Ambient Dataloops: Generative Models for Dataset Refinement
21. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
22. ICML 2026An Interactive Paradigm for Deep Research
23. ICML 2026Analytic Bijections for Smooth and Interpretable Normalizing Flows
24. ICML 2026Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
25. ICML 2026AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
26. ICML 2026Anytime Safe PAC Efficient Reasoning
27. ICML 2026ARC-Decode: Accelerated Decoding with Risk-Bounded Acceptance
28. ICML 2026ArcDAE: Asymmetric Rectified Contrastive Diffusion Autoencoder for Unified Representation Learning
29. ICML 2026Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach
30. ICML 2026AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
31. ICML 2026ASIR: Steganography for Diffusion Models via Antipodal Sampling and Iterative Recovery
32. ICML 2026At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
33. ICML 2026Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
34. ICML 2026Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
35. ICML 2026Attention Sparsity is Input-Stable: Training-Free Sparse Attention for Video Generation via Offline Sparsity Profiling and Online QK Co-Clustering
36. ICML 2026Automatic Layer Selection for Hallucination Detection
37. ICML 2026Autoregression with Self-Token Prediction
38. ICML 2026Autoregressive Image Generation with Masked Bit Modeling
39. ICML 2026AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents
40. ICML 2026Balancing Plasticity and Stability with Fast and Slow Successor Features
41. ICML 2026Base Models Know How to Reason, Thinking Models Learn When
42. ICML 2026Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
43. ICML 2026Being More Lightweight and Practical: Mini-sized Contrastive Learning Pre-trained Models for Fine-grained Traffic Task
44. ICML 2026Benchmarking and Improving Fine-Grained Text-to-Image Alignment via Paired Reinforcement Learning
45. ICML 2026Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
46. ICML 2026Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion
47. ICML 2026Beyond Looking Up, Try Looking Around: Harmonizing Global Structure and Local Consistency in Optimal Transport for Short Text Clustering
48. ICML 2026Beyond Pixel Histories: World Models with Persistent 3D State
49. ICML 2026Beyond Reactivity: Proactive Adaptive Conformal Inference for Online LLM Factuality
50. ICML 2026Bi-Anchor Interpolation Solver for Accelerating Generative Modeling

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。