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 1,304 篇，第 24 / 27 页
- ICML 2023Hindsight Learning for MDPs with Exogenous Inputs
- ICML 2023How much does Initialization Affect Generalization?
- ICML 2023Hyperbolic Representation Learning: Revisiting and Advancing
- ICML 2023Hyperparameters in Reinforcement Learning and How To Tune Them
- ICML 2023Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
- ICML 2023Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
- ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
- ICML 2023Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
- ICML 2023Individually Fair Learning with One-Sided Feedback
- ICML 2023Inflow, Outflow, and Reciprocity in Machine Learning
- ICML 2023Input Perturbation Reduces Exposure Bias in Diffusion Models
- ICML 2023Jump-Start Reinforcement Learning
- ICML 2023KDEformer: Accelerating Transformers via Kernel Density Estimation
- ICML 2023Latent Traversals in Generative Models as Potential Flows
- ICML 2023LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
- ICML 2023Learn to Accumulate Evidence from All Training Samples: Theory and Practice
- ICML 2023Learning Belief Representations for Partially Observable Deep RL
- ICML 2023Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
- ICML 2023Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
- ICML 2023Learning Globally Smooth Functions on Manifolds
- ICML 2023Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
- ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
- ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
- ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
- ICML 2023Learning the Dynamics of Sparsely Observed Interacting Systems
- ICML 2023Learning to Design Analog Circuits to Meet Threshold Specifications
- ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
- ICML 2023Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
- ICML 2023Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
- ICML 2023Learning Unnormalized Statistical Models via Compositional Optimization
- ICML 2023Learning useful representations for shifting tasks and distributions
- ICML 2023Learning-augmented private algorithms for multiple quantile release
- ICML 2023Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
- ICML 2023LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
- ICML 2023Lower Bounds for Learning in Revealing POMDPs
- ICML 2023MALTS: Matching After Learning to Stretch
- ICML 2023Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
- ICML 2023Markovian Gaussian Process Variational Autoencoders
- ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
- ICML 2023Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
- ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
- ICML 2023Metagenomic Binning using Connectivity-constrained Variational Autoencoders
- ICML 2023Minimal Width for Universal Property of Deep RNN
- ICML 2023Minimizing Trajectory Curvature of ODE-based Generative Models
- ICML 2023Model Transferability with Responsive Decision Subjects
- ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
- ICML 2023MODeL: Memory Optimizations for Deep Learning
- ICML 2023MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
- ICML 2023Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
- ICML 2023Multi-View Masked World Models for Visual Robotic Manipulation
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。