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 303 篇，第 4 / 7 页
- ICLR 2026Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing
- ICLR 2026Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
- ICLR 2026LABEL-FREE MITIGATION OF SPURIOUS CORRELATIONS IN VLMS USING SPARSE AUTOENCODERS
- ICLR 2026Latent Denoising Makes Good Tokenizers
- ICLR 2026Latent Diffusion Model without Variational Autoencoder
- ICLR 2026Latent Stochastic Interpolants
- ICLR 2026Latent Wavelet Diffusion For Ultra High-Resolution Image Synthesis
- ICLR 2026LayerSync: Self-aligning Intermediate Layers
- ICLR 2026Learn to Guide Your Diffusion Model
- ICLR 2026Learnable Sparsity for Vision Generative Models
- ICLR 2026Learning a distance measure from the information-estimation geometry of data
- ICLR 2026Learning Correlated Reward Models: Statistical Barriers and Opportunities
- ICLR 2026Learning multimodal dictionary decompositions with group-sparse autoencoders
- ICLR 2026Learning on a Razor’s Edge: Identifiability and Singularity of Polynomial Neural Networks
- ICLR 2026Learning Retrieval Models with Sparse Autoencoders
- ICLR 2026Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement
- ICLR 2026Light-X: Generative 4D Video Rendering with Camera and Illumination Control
- ICLR 2026LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
- ICLR 2026LLM Pretraining with Continuous Concepts
- ICLR 2026LLMs Can Hide Text in Other Text of the Same Length
- ICLR 2026Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
- ICLR 2026LS-Merge: Merging Language Models in Latent Space
- ICLR 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry
- ICLR 2026MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
- ICLR 2026Masked Generative Policy for Robotic Control
- ICLR 2026Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
- ICLR 2026Matched Data, Better Models: Target Aligned Data Filtering with Sparse Features
- ICLR 2026Measurement Score-Based Diffusion Model
- ICLR 2026MemGen: Weaving Generative Latent Memory for Self-Evolving Agents
- ICLR 2026Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains
- ICLR 2026Mitigating Noise Shift in Denoising Generative Models with Noise Awareness Guidance
- ICLR 2026Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
- ICLR 2026Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter
- ICLR 2026MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling
- ICLR 2026Motion Prior Distillation in Time Reversal Sampling for Generative Inbetweening
- ICLR 2026Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
- ICLR 2026Multiverse Mechanica: A Testbed for Learning Game Mechanics via Counterfactual Worlds
- ICLR 2026MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
- ICLR 2026MVCustom: Multi-View Customized Diffusion via Geometric Latent Rendering and Completion
- ICLR 2026Navigating the Latent Space Dynamics of Neural Models
- ICLR 2026NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
- ICLR 2026Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
- ICLR 2026NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning
- ICLR 2026NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
- ICLR 2026On Discriminative vs. Generative classifiers: Rethinking MLLMs for Action Understanding
- ICLR 2026On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
- ICLR 2026Online Black-Box Prompt Optimization with Regret Guarantees under Noisy Feedback
- ICLR 2026Online Decision Making with Generative Action Sets
- ICLR 2026Overshoot and Shrinkage in Classifier-Free Guidance: From Theory to Practice
- ICLR 2026P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
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。