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 21 / 87 页
- ICML 2026Text Generation as Continuous Latent Dynamics via Reinforcement Learning
- ICML 2026The Accumulation of Score Estimation Error in Diffusion Models
- ICML 2026The Convergent Representation of Contrastive Vision-Language Models: Geometry, Modality Gap and Shared Space Alignment
- ICML 2026The Data Manifold under the Microscope
- ICML 2026The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
- ICML 2026The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity
- ICML 2026The Latent Color Subspace: Emergent Order in High-Dimensional Chaos
- ICML 2026The Velocity Deficit: Initial Energy Injection for Flow Matching
- ICML 2026Thinking with Geometry: Active Geometry Integration for Spatial Reasoning
- ICML 2026Threshold-Guided Optimization for Visual Generative Models
- ICML 2026Tightening the Score Matching Gap for Diffusion Models
- ICML 2026Tilt Matching for Scalable Sampling and Fine-Tuning
- ICML 2026Time-Conditioned Foreseeing: An EHR-Specific Foundation Model for Irregular Dynamics and Calendrical Time
- ICML 2026Timestep Rescheduling in Diffusion Inversion
- ICML 2026TMD-Bench: A Multi-Level Evaluation Paradigm for Music–Dance Co-Generation
- ICML 2026Toward Identifiable Sparse Autoencoders
- ICML 2026Towards A Generative Protein Evolution Machine with DPLM-Evo
- ICML 2026Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
- ICML 2026Towards Generative Graph Matching for Graph Edit Distance Computation
- ICML 2026Towards Spectroscopy: Susceptibility Clusters in Language Models
- ICML 2026Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm
- ICML 2026Training-Free Adaptation of Diffusion Models via Doob's $h$-Transform
- ICML 2026Training-Free Bayesian Filtering with Generative Emulators
- ICML 2026Training-Free Rate-Distortion-Perception Traversal With Diffusion
- ICML 2026Training-Free Vector Quantization via Gaussian VAEs
- ICML 2026Trajectory Consistency for One-Step Generation on Euler Mean Flows
- ICML 2026Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
- ICML 2026Trajectory-Aware Spiking DiTs Conversion via Membrane Potential Error-Feedback
- ICML 2026TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
- ICML 2026Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
- ICML 2026Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
- ICML 2026TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
- ICML 2026Twins: Learn to Predict Unified Representations with Focal Loss
- ICML 2026Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
- ICML 2026Two-Parameter Flows for Learning Population Dynamics of Physical Systems
- ICML 2026UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
- ICML 2026Uncovering Competency Gaps in Large Language Models and Their Benchmarks
- ICML 2026Understanding LoRA as Knowledge Memory: An Empirical Analysis
- ICML 2026Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
- ICML 2026Understanding Transfer Learning of RNA Foundation Models on Downstream Tasks
- ICML 2026Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
- ICML 2026UnHype: CLIP-Guided Hypernetworks for Dynamic LoRA Unlearning
- ICML 2026UniFLoW: Universal Multi-Modal Federated LoRA Fine-Tuning Framework with Analytical Aggregation
- ICML 2026Unifying Masked Diffusion Models with Various Generation Orders and Beyond
- ICML 2026Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
- ICML 2026Unpaired Visual Editing with Self-Consistent Flow Matching
- ICML 2026Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
- ICML 2026Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization
- ICML 2026Value-as-Return: A Two-Stage Framework to Align on the Optimal Score Function
- ICML 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
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。