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 6 / 7 页
- ICLR 2026Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
- ICLR 2026SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
- ICLR 2026SpikeGen: Decoupled “Rods and Cones” Visual Representation Processing with Latent Generative Framework
- ICLR 2026Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models
- ICLR 2026STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
- ICLR 2026STORK: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence
- ICLR 2026Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
- ICLR 2026Subquadratic Algorithms and Hardness for Attention with Any Temperature
- ICLR 2026Symmetric Space Learning for Combinatorial Generalization
- ICLR 2026Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
- ICLR 2026Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models
- ICLR 2026Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
- ICLR 2026Taming Polysemanticity in LLMs: Theory-Grounded Feature Recovery via Sparse Autoencoders
- ICLR 2026Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
- ICLR 2026TAVAE: A VAE with Adaptable Priors Explains Contextual Modulation in the Visual Cortex
- ICLR 2026Teaching Metric Distance to Discrete Autoregressive Language Models
- ICLR 2026Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
- ICLR 2026Terminal Velocity Matching
- ICLR 2026Test-Time Scaling with Reflective Generative Model
- ICLR 2026Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
- ICLR 2026The Diffusion Duality, Chapter II: $\Psi$-Samplers and Efficient Curriculum
- ICLR 2026The Intricate Dance of Prompt Complexity, Quality, Diversity and Consistency in T2I Models
- ICLR 2026The Lattice Geometry of Neural Network Quantization: A Short Equivalence Proof of GPTQ and Babai's Algorithm
- ICLR 2026The Price of Amortized inference in Sparse Autoencoders
- ICLR 2026The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology
- ICLR 2026The Spacetime of Diffusion Models: An Information Geometry Perspective
- ICLR 2026There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models
- ICLR 2026Think Then Embed: Generative Context Improves Multimodal Embedding
- ICLR 2026Time-Gated Multi-Scale Flow Matching for Time-Series Imputation
- ICLR 2026ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing
- ICLR 2026Topological Flow Matching
- ICLR 2026Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders
- ICLR 2026Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
- ICLR 2026TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models
- ICLR 2026Tree-sliced Sobolev IPM
- ICLR 2026TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models
- ICLR 2026Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
- ICLR 2026Two-Layer Convolutional Autoencoders Trained on Normal Data Provably Detect Unseen Anomalies
- ICLR 2026Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
- ICLR 2026Unsupervised Representation Learning for 3D Mesh Parameterization with Semantic and Visibility Objectives
- ICLR 2026Untraceable DeepFakes via Traceable Fingerprint Elimination
- ICLR 2026Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling
- ICLR 2026Verification of the Implicit World Model in a Generative Model via Adversarial Sequences
- ICLR 2026VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
- ICLR 2026What Exactly Does Guidance Do in Masked Discrete Diffusion Models
- ICLR 2026What Generative Search Engines Like and How to Optimize Web Content Cooperatively
- ICLR 2026What happens when generative AI models train recursively on each others' outputs?
- ICLR 2026When LLMs get significantly worse: A statistical approach to detect model degradations
- ICLR 2026When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
- ICLR 2026WithAnyone: Toward Controllable and ID Consistent Image Generation
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。