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 19 / 87 页
- ICML 2026Reconstructing Template-Memorized Images from Natural Prompts
- ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
- ICML 2026ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
- ICML 2026Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
- ICML 2026RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
- ICML 2026Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics
- ICML 2026Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
- ICML 2026Rényi Diffusion Models
- ICML 2026RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
- ICML 2026Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
- ICML 2026Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
- ICML 2026ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
- ICML 2026Rethinking 3D Shape Generation: Diffusion over Superquadrics
- ICML 2026Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
- ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
- ICML 2026Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
- ICML 2026Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
- ICML 2026Rethinking Visual Intelligence: Insights from Video Pretraining
- ICML 2026RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
- ICML 2026Revisiting Spectral Representations in Generative Diffusion Models
- ICML 2026RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
- ICML 2026Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
- ICML 2026Riemannian MeanFlow
- ICML 2026Riemannian MeanFlow for One-Step Generation on Manifolds
- ICML 2026Riemannian Neural Optimal Transport
- ICML 2026RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
- ICML 2026RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
- ICML 2026Robust and Consistent Ski Rental with Distributional Advice
- ICML 2026Robust Cross-Modal Retrieval via Generative Semantic Refinement and Exclusion-Guided Adaptation
- ICML 2026Robust Parallel Diffusion Sampling via Dynamic Jacobian Bandwidth
- ICML 2026Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
- ICML 2026RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
- ICML 2026SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
- ICML 2026SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos
- ICML 2026Scalable GANs with Transformers
- ICML 2026Scalable RF Simulation in Generative 4D Worlds
- ICML 2026Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
- ICML 2026SCalDA: Semantics-Calibrated and Diffusion-Enhanced Data Augmentation
- ICML 2026ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression
- ICML 2026SceneDirector: Bridging Explicit Geometry and Generative Priors for Unified Driving Scene Editing
- ICML 2026Score Based Error Correcting Code Decoder
- ICML 2026Score Correction for Generative Models with Probabilistic Constraints
- ICML 2026SDiD：Shared diffusion prior for efficient distributed stereo image compression
- ICML 2026SE(3)-Equivariant Flow Matching with Gaussian Process Priors for Geometric Trajectory Prediction
- ICML 2026SE(n)-Invariant Flow Matching: A General Framework with Application to Object Reassembly
- ICML 2026Seeing the Unseen: Physics-as-Representation for Generalizable Gaze Perception
- ICML 2026SegPVSG: Panoptic Video Scene Graph Generation via Temporal Focusing and Generative Augmentation
- ICML 2026Self-Refining Video Sampling
- ICML 2026Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
- ICML 2026Semantic Editing with Coupled Stochastic Differential Equations
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。