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 86 / 87 页
- NeurIPS 2023Topological Parallax: A Geometric Specification for Deep Perception Models
- NeurIPS 2023TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
- NeurIPS 2023Toward Understanding Generative Data Augmentation
- NeurIPS 2023Towards a Unified Framework of Contrastive Learning for Disentangled Representations
- NeurIPS 2023Towards Automated Circuit Discovery for Mechanistic Interpretability
- NeurIPS 2023Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
- NeurIPS 2023Towards Free Data Selection with General-Purpose Models
- NeurIPS 2023Towards robust and generalizable representations of extracellular data using contrastive learning
- NeurIPS 2023Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
- NeurIPS 2023Towards Test-Time Refusals via Concept Negation
- NeurIPS 2023Towards Unbounded Machine Unlearning
- NeurIPS 2023Trade-off Between Efficiency and Consistency for Removal-based Explanations
- NeurIPS 2023Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
- NeurIPS 2023Training Energy-Based Normalizing Flow with Score-Matching Objectives
- NeurIPS 2023Training Private Models That Know What They Don’t Know
- NeurIPS 2023Training Transitive and Commutative Multimodal Transformers with LoReTTa
- NeurIPS 2023Trans-Dimensional Generative Modeling via Jump Diffusion Models
- NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
- NeurIPS 2023Transformer-based Planning for Symbolic Regression
- NeurIPS 2023Tree Variational Autoencoders
- NeurIPS 2023Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
- NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
- NeurIPS 2023TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
- NeurIPS 2023Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
- NeurIPS 2023Unbiased learning of deep generative models with structured discrete representations
- NeurIPS 2023Uncertainty Quantification via Neural Posterior Principal Components
- NeurIPS 2023Uncertainty-Aware Instance Reweighting for Off-Policy Learning
- NeurIPS 2023Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
- NeurIPS 2023Understanding and Improving Feature Learning for Out-of-Distribution Generalization
- NeurIPS 2023Understanding and Mitigating Copying in Diffusion Models
- NeurIPS 2023Understanding Contrastive Learning via Distributionally Robust Optimization
- NeurIPS 2023Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes
- NeurIPS 2023Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
- NeurIPS 2023Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
- NeurIPS 2023Undirected Probabilistic Model for Tensor Decomposition
- NeurIPS 2023Unifying GANs and Score-Based Diffusion as Generative Particle Models
- NeurIPS 2023Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
- NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
- NeurIPS 2023Unsupervised Image Denoising with Score Function
- NeurIPS 2023Use perturbations when learning from explanations
- NeurIPS 2023VanillaNet: the Power of Minimalism in Deep Learning
- NeurIPS 2023Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
- NeurIPS 2023VeriX: Towards Verified Explainability of Deep Neural Networks
- NeurIPS 2023Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
- NeurIPS 2023Video Prediction Models as Rewards for Reinforcement Learning
- NeurIPS 2023VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception
- NeurIPS 2023Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
- NeurIPS 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
- NeurIPS 2023What Do Deep Saliency Models Learn about Visual Attention?
- NeurIPS 2023What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization 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。