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 1,216 篇，第 18 / 25 页
- NeurIPS 2023Lexinvariant Language Models
- NeurIPS 2023LICO: Explainable Models with Language-Image COnsistency
- NeurIPS 2023LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
- NeurIPS 2023List and Certificate Complexities in Replicable Learning
- NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
- NeurIPS 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
- NeurIPS 2023Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
- NeurIPS 2023Loss Decoupling for Task-Agnostic Continual Learning
- NeurIPS 2023Loss Dynamics of Temporal Difference Reinforcement Learning
- NeurIPS 2023Lossy Image Compression with Conditional Diffusion Models
- NeurIPS 2023Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
- NeurIPS 2023Markovian Sliced Wasserstein Distances: Beyond Independent Projections
- NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
- NeurIPS 2023Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
- NeurIPS 2023Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
- NeurIPS 2023Maximum State Entropy Exploration using Predecessor and Successor Representations
- NeurIPS 2023May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
- NeurIPS 2023MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
- NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
- NeurIPS 2023Meet in the Middle: A New Pre-training Paradigm
- NeurIPS 2023Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
- NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
- NeurIPS 2023Metis: Understanding and Enhancing In-Network Regular Expressions
- NeurIPS 2023Metropolis Sampling for Constrained Diffusion Models
- NeurIPS 2023MGDD: A Meta Generator for Fast Dataset Distillation
- NeurIPS 2023Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
- NeurIPS 2023Minimum Description Length and Generalization Guarantees for Representation Learning
- NeurIPS 2023Mirror Diffusion Models for Constrained and Watermarked Generation
- NeurIPS 2023Mitigating Test-Time Bias for Fair Image Retrieval
- NeurIPS 2023Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
- NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
- NeurIPS 2023Mnemosyne: Learning to Train Transformers with Transformers
- NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
- NeurIPS 2023Model Spider: Learning to Rank Pre-Trained Models Efficiently
- NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
- NeurIPS 2023Model-enhanced Vector Index
- NeurIPS 2023Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
- NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
- NeurIPS 2023Multi Time Scale World Models
- NeurIPS 2023Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
- NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
- NeurIPS 2023Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
- NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
- NeurIPS 2023NAP: Neural 3D Articulated Object Prior
- NeurIPS 2023NAS-X: Neural Adaptive Smoothing via Twisting
- NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
- NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
- NeurIPS 2023Near-optimal learning with average Hölder smoothness
- NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
- NeurIPS 2023Neural Image Compression: Generalization, Robustness, and Spectral Biases
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。