深度学习 · generative models and autoencoders论文趋势 2023–2026
ICML/NeurIPS 深度学习方向：generative_models_and_autoencoders
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 4,314 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 776 | 78 | 286 | 76 | 1,216 |
| 2024 | 349 | 174 | 139 | 106 | 768 |
| 2025 | 240 | 275 | 275 | 110 | 900 |
| 2026 | 349 | 303 | 604 | 174 | 1,430 |
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 604 篇，第 6 / 13 页
- ICML 2026Information-Theoretic Generalization Bounds for VAEs: A Role of Encoder and Latent Variable
- ICML 2026InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
- ICML 2026Insertion Based Sequence Generation with Learnable Order Dynamics
- ICML 2026INT vs. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
- ICML 2026Integrated Episodic and Semantic Memory via Modulating Transformer FeedForward Layers
- ICML 2026Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
- ICML 2026Interpreting Genomic Language Models using Sparse Autoencoders
- ICML 2026Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
- ICML 2026Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
- ICML 2026Is Generation Required for Data-Efficient Perception?
- ICML 2026Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models
- ICML 2026Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
- ICML 2026Just Y-Prediction: Enabling Historical Cumulative Inconsistency in Label Diffusion for Learning with Noisy Label
- ICML 2026KIStego: Key-Independent Secure Image Distribution via Bipartite Structural Invariants
- ICML 2026Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
- ICML 2026Language Generation in the Limit: Complexity Barriers and Implications for Learning
- ICML 2026Large-capacity and Receiver Authenticable Generative Image Steganography
- ICML 2026Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
- ICML 2026Latent-Guided Cooperative Energy-Based Models
- ICML 2026LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
- ICML 2026LayerT2V: A Unified Multi-Layer Video Generation Framework
- ICML 2026Learning a Generative Meta-Model of LLM Activations
- ICML 2026Learning Coherent Representations: A Topological Approach to Interpretability
- ICML 2026Learning Discrete Diffusion on Graphs via Free-Energy Gradient Flows
- ICML 2026Learning Manifold Data with Flow Matching
- ICML 2026Learning Permutation Distributions via Reflected Diffusion on Ranks
- ICML 2026Learning to Remember, Learn, and Forget in Attention-Based Models
- ICML 2026Learning to Watermark in the Latent Space of Generative Models
- ICML 2026Learning-to-Optimize via Deep Unfolded Flows
- ICML 2026LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games
- ICML 2026Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
- ICML 2026Let Language Constrain Geometry: Vision–Language Models as Semantic and Spatial Critics for 3D Generation
- ICML 2026Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
- ICML 2026LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
- ICML 2026Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention
- ICML 2026Likelihood Matching for Diffusion Models
- ICML 2026Likelihood over Estimation: Robust Quadratic Discriminant Analysis for Heavy-Tailed Distributions with Theory and Evidence
- ICML 2026LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation
- ICML 2026LipoPU: Pocket-level Prediction of Lipid-Protein Interactions via Positive-Unlabeled Learning
- ICML 2026LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
- ICML 2026Local Mechanisms of Compositional Generalization in Conditional Diffusion
- ICML 2026Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences
- ICML 2026Locally Coherent Parallel Decoding in Diffusion Language Models
- ICML 2026Logical Guidance for the Exact Composition of Diffusion Models
- ICML 2026LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
- ICML 2026Lookahead Path Likelihood Optimization for Diffusion LLMs
- ICML 2026Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
- ICML 2026Lookahead Unmasking Elicits Reliable Decoding in Diffusion Language Models
- ICML 2026LUCID: Attention with Preconditioned Representations
- ICML 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查深度学习 · generative models and autoencoders在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "dl-generative-models-and-autoencoders"}
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。