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 349 篇，第 4 / 7 页
- NeurIPS 2026Inferring how internal brain state shapes neural responses with state-dependent diffusion models
- NeurIPS 2026Information-Geometric Forward Policy Training in GFlowNets
- NeurIPS 2026Invariance and Body-Order Compose Additively: Minimax Rates on $\mathrm{SO}(3)^n$
- NeurIPS 2026Is a Linear Probe Evidence of a Linear Representation?
- NeurIPS 2026Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models
- NeurIPS 2026JaSpec-LID: Local Intrinsic Dimension Estimation via Jacobian Spectra of ODE-based Generative Models
- NeurIPS 2026Kernel-Gradient Drifting Models
- NeurIPS 2026Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting
- NeurIPS 2026Latent Process Generator Matching
- NeurIPS 2026LayoutBridge: Anisotropic Brownian Bridges for Public Indoor Floorplan Generation
- NeurIPS 2026Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
- NeurIPS 2026Learn from your own latents and not from tokens: A sample-complexity theory
- NeurIPS 2026Learning a Unified Cross-Model Semantic Dictionary via Gated Bottleneck Sparse Autoencoders
- NeurIPS 2026Learning Distributions from Multiple Data Providers
- NeurIPS 2026Learning Generative Dynamics for 3D Molecule Generation via Sequential Neuro-Symbolic Constraints
- NeurIPS 2026Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval
- NeurIPS 2026Learning to Solve Generative ODEs Beyond the Linear Span
- NeurIPS 2026Less is More: Fewer Tokens and Blocks Make AIGI Detection Faster and More Generalizable
- NeurIPS 2026Likelihood-Free Generative Policy Optimization
- NeurIPS 2026LLM-Auction: Generative Auction towards LLM-Native Advertising
- NeurIPS 2026Localizing and Repairing Sparse-Prompt Failure in SAM Decoders via Box-to-Point Counterfactual
- NeurIPS 2026Looking Through the Mirror: Minimax-Optimal Regularized Regrets in Online Learning and Bandits
- NeurIPS 2026Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
- NeurIPS 2026LoRAtorio: An intrinsic approach to LoRA Skill Composition
- NeurIPS 2026Magnetic Resonance Unpaired Image Translation with Pseudometric Schrödinger Bridges
- NeurIPS 2026Masked Generative Pretraining Improves Cross-Dataset Transfer in Pixel-Space Diffusion
- NeurIPS 2026Matryoshka Transcoders and Hierarchy Misalignment: When SAE Absorption Protection Does Not Transfer
- NeurIPS 2026Meta Inverse Prompting for Video Generative Models
- NeurIPS 2026MIND: Monge Inception Distance for Generative Models Evaluation
- NeurIPS 2026MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
- NeurIPS 2026MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series
- NeurIPS 2026Mixed-Curvature Geometric Latent Diffusion Model for Graph Generation
- NeurIPS 2026Model Collapse is a Singular Complexity Trajectory
- NeurIPS 2026Model-Based Online Decision Making via Generative Trajectory Planning
- NeurIPS 2026Module Specialization in Transformer Factual Recall under Correlated Fact Distributions
- NeurIPS 2026MOSAIC: Scaling Long-Horizon Language Agents via Multi-Scale Adaptive Inference Control
- NeurIPS 2026Multi-step Consistency Models: A Complete Error Theory and Optimal Step Selection
- NeurIPS 2026Multilevel and Sequential Monte Carlo for Training-Free Diffusion Guidance
- NeurIPS 2026NAGO: Noise-Aware Generative Operator via Flow Matching
- NeurIPS 2026Nahual: A Sequence Model for Language and Atoms
- NeurIPS 2026Network Learning with Semi-relaxed Gromov-Wasserstein
- NeurIPS 2026Neural Backward Filtering Forward Guiding
- NeurIPS 2026Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction
- NeurIPS 2026Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries
- NeurIPS 2026Neuromodulated Constrained Autoencoders for Context-Dependent Manifold Learning
- NeurIPS 2026NIV: Neural Axis Variations for Variable Font Generation
- NeurIPS 2026No Model Required: Text Entropy Rate Filtering Prevents Iterative Fine-Tuning Collapse
- NeurIPS 2026Noise-Started One-Step Image Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
- NeurIPS 2026Noisy2Latent: Nonparametric Deconvolution and Denoising via Maximum Mean Discrepancy
- NeurIPS 2026Non-Expanding Gated Continued Fraction Architecture for Feedforward Layers in Language Models
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。