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 240 篇，第 5 / 5 页
- NeurIPS 2025Stability and Oracle Inequalities for Optimal Transport Maps between General Distributions
- NeurIPS 2025Steering Generative Models with Experimental Data for Protein Fitness Optimization
- NeurIPS 2025StegoZip: Enhancing Linguistic Steganography Payload in Practice with Large Language Models
- NeurIPS 2025StruDiCO: Structured Denoising Diffusion with Gradient-free Inference-stage Boosting for Memory and Time Efficient Combinatorial Optimization
- NeurIPS 2025SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction
- NeurIPS 2025System-Embedded Diffusion Bridge Models
- NeurIPS 2025Taming generative video models for zero-shot optical flow extraction
- NeurIPS 2025TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
- NeurIPS 2025TC-Light: Temporally Coherent Generative Rendering for Realistic World Transfer
- NeurIPS 2025The Generative Leap: Tight Sample Complexity for Efficiently Learning Gaussian Multi-Index Models
- NeurIPS 2025The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
- NeurIPS 2025ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation
- NeurIPS 2025Towards a Pairwise Ranking Model with Orderliness and Monotonicity for Label Enhancement
- NeurIPS 2025Towards A Translative Model of Sperm Whale Vocalization
- NeurIPS 2025Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
- NeurIPS 2025Training-Free Safe Denoisers for Safe Use of Diffusion Models
- NeurIPS 2025Transferring Linear Features Across Language Models With Model Stitching
- NeurIPS 2025Transformer Key-Value Memories Are Nearly as Interpretable as Sparse Autoencoders
- NeurIPS 2025Transformers for Mixed-type Event Sequences
- NeurIPS 2025Transition Matching: Scalable and Flexible Generative Modeling
- NeurIPS 2025TreeGen: A Bayesian Generative Model for Hierarchies
- NeurIPS 2025UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis
- NeurIPS 2025un$^2$CLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP
- NeurIPS 2025Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
- NeurIPS 2025Universal Video Temporal Grounding with Generative Multi-modal Large Language Models
- NeurIPS 2025Unveiling Concept Attribution in Diffusion Models
- NeurIPS 2025UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation
- NeurIPS 2025Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
- NeurIPS 2025Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
- NeurIPS 2025VisDiff: SDF-Guided Polygon Generation for Visibility Reconstruction, Characterization and Recognition
- NeurIPS 2025Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting
- NeurIPS 2025Wasserstein Convergence of Critically Damped Langevin Diffusions
- NeurIPS 2025What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
- NeurIPS 2025When and how can inexact generative models still sample from the data manifold?
- NeurIPS 2025When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective
- NeurIPS 2025When Models Don’t Collapse: On the Consistency of Iterative MLE
- NeurIPS 2025Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
- NeurIPS 2025Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
- NeurIPS 2025Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
- NeurIPS 2025WritingBench: A Comprehensive Benchmark for Generative Writing
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。