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 4,314 篇，第 77 / 87 页
- NeurIPS 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
- NeurIPS 2023Facing Off World Model Backbones: RNNs, Transformers, and S4
- NeurIPS 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
- NeurIPS 2023Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
- NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
- NeurIPS 2023Fast Attention Requires Bounded Entries
- NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
- NeurIPS 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
- NeurIPS 2023Fast Trainable Projection for Robust Fine-tuning
- NeurIPS 2023Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
- NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
- NeurIPS 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
- NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
- NeurIPS 2023Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
- NeurIPS 2023Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
- NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
- NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
- NeurIPS 2023Fine-Tuning Language Models with Just Forward Passes
- NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
- NeurIPS 2023First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
- NeurIPS 2023Flow Factorized Representation Learning
- NeurIPS 2023Flow: Per-instance Personalized Federated Learning
- NeurIPS 2023Formulating Discrete Probability Flow Through Optimal Transport
- NeurIPS 2023FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
- NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
- NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
- NeurIPS 2023Fundamental Limits and Tradeoffs in Invariant Representation Learning
- NeurIPS 2023Game Solving with Online Fine-Tuning
- NeurIPS 2023Gaussian Mixture Solvers for Diffusion Models
- NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
- NeurIPS 2023Generalized Information-theoretic Multi-view Clustering
- NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
- NeurIPS 2023Generalizing Nonlinear ICA Beyond Structural Sparsity
- NeurIPS 2023Generating QM1B with PySCF$_{\text{IPU}}$
- NeurIPS 2023Generative Category-level Object Pose Estimation via Diffusion Models
- NeurIPS 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
- NeurIPS 2023Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
- NeurIPS 2023Generative Neural Fields by Mixtures of Neural Implicit Functions
- NeurIPS 2023Generator Born from Classifier
- NeurIPS 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
- NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
- NeurIPS 2023Geometry-Aware Adaptation for Pretrained Models
- NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
- NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
- NeurIPS 2023Global Optimality in Bivariate Gradient-based DAG Learning
- NeurIPS 2023Globally injective and bijective neural operators
- NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
- NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
- NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
- NeurIPS 2023Grassmann Manifold Flows for Stable Shape Generation
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。