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 12 / 87 页
- ICML 2026Causal Representation Learning with Optimal Compression and Complex Treatments
- ICML 2026Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
- ICML 2026CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
- ICML 2026Co-Generative De Novo Functional Protein Design
- ICML 2026CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation
- ICML 2026CoFrGeNet: Continued Fraction Architectures for Language Generation
- ICML 2026CoGenCast: A Coupled Autoregressive–Flow Generative Framework for Time Series Forecasting
- ICML 2026CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
- ICML 2026ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
- ICML 2026Compositional Generative Modeling from Decentralized Data
- ICML 2026Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
- ICML 2026Computational Arbitrage in AI Model Markets
- ICML 2026Compute Where it Counts: Self Optimizing Language Models
- ICML 2026Concept Removal for Frontier Image Generative Models
- ICML 2026Concept-Guided Tokenization: Closing the Gap Between Reconstruction and Generation
- ICML 2026Condition-Aware Graph Flow Matching for Modeling the Distributions of Complex Fluid Systems
- ICML 2026Conf-Gen: Conformal Uncertainty Quantification for Generative Models
- ICML 2026Confidence is Not Universal: Task-Dependent Calibration and Emergent Behavior in LLMs
- ICML 2026Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
- ICML 2026Conformal C2ST: Turning weak classifiers into strong two-sample tests
- ICML 2026Consistent Diffusion Language Models
- ICML 2026Content-Style Identification via Differential Independence
- ICML 2026Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
- ICML 2026Contrastive Flow Map Matching
- ICML 2026ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
- ICML 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
- ICML 2026CountsDiff: A diffusion model on the natural numbers for generation and imputation of count-based data
- ICML 2026CRAG: Can 3D Generative Models Help 3D Assembly?
- ICML 2026CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM
- ICML 2026Curated Synthetic Data Doesn’t Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
- ICML 2026d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation
- ICML 2026DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
- ICML 2026DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
- ICML 2026Decomposing Out-of-Distribution Error in Conditional Flow Matching via Wasserstein Geometry
- ICML 2026Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
- ICML 2026Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression
- ICML 2026Deformba: Vision State Space Model with Adaptive State Fusion
- ICML 2026DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
- ICML 2026Detecting Perspective Shifts in Multi-Agent Systems
- ICML 2026Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
- ICML 2026DiasR: Dual-Modal Identity-Anchored Sparse Routing for Efficient Multi-Subject Video Generation
- ICML 2026DiFA: Inference-Time Forward-Process Alignment for Diffusion Models
- ICML 2026Differential syntactic and semantic encoding in LLMs
- ICML 2026DiffStyle3D: Consistent 3D Gaussian Stylization via Attention Optimization
- ICML 2026Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis
- ICML 2026Diffusion Controller: Framework, Algorithms and Parameterization
- ICML 2026Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
- ICML 2026Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
- ICML 2026Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
- ICML 2026Dimension-free convergence of diffusion models for approximate Gaussian mixtures
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。