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 768 篇，第 14 / 16 页
- NeurIPS 2024PaCE: Parsimonious Concept Engineering for Large Language Models
- NeurIPS 2024PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
- NeurIPS 2024Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
- NeurIPS 2024Parametric model reduction of mean-field and stochastic systems via higher-order action matching
- NeurIPS 2024Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
- NeurIPS 2024PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision
- NeurIPS 2024PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
- NeurIPS 2024PERIA: Perceive, Reason, Imagine, Act via Holistic Language and Vision Planning for Manipulation
- NeurIPS 2024Pessimistic Backward Policy for GFlowNets
- NeurIPS 2024Phased Consistency Models
- NeurIPS 2024PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
- NeurIPS 2024Piecewise deterministic generative models
- NeurIPS 2024Poisson Variational Autoencoder
- NeurIPS 2024Polyhedral Complex Derivation from Piecewise Trilinear Networks
- NeurIPS 2024Practical Shuffle Coding
- NeurIPS 2024Pricing and Competition for Generative AI
- NeurIPS 2024ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
- NeurIPS 2024Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
- NeurIPS 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
- NeurIPS 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
- NeurIPS 2024PTQ4DiT: Post-training Quantization for Diffusion Transformers
- NeurIPS 2024PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
- NeurIPS 2024QGFN: Controllable Greediness with Action Values
- NeurIPS 2024QTIP: Quantization with Trellises and Incoherence Processing
- NeurIPS 2024Questioning the Survey Responses of Large Language Models
- NeurIPS 2024Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
- NeurIPS 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
- NeurIPS 2024Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
- NeurIPS 2024RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
- NeurIPS 2024Rethinking Score Distillation as a Bridge Between Image Distributions
- NeurIPS 2024Retrieval-Augmented Diffusion Models for Time Series Forecasting
- NeurIPS 2024Return of Unconditional Generation: A Self-supervised Representation Generation Method
- NeurIPS 2024Right this way: Can VLMs Guide Us to See More to Answer Questions?
- NeurIPS 2024RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts
- NeurIPS 2024S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
- NeurIPS 2024Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
- NeurIPS 2024Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
- NeurIPS 2024Score-based generative models are provably robust: an uncertainty quantification perspective
- NeurIPS 2024SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
- NeurIPS 2024SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers
- NeurIPS 2024Search for Efficient Large Language Models
- NeurIPS 2024Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
- NeurIPS 2024Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
- NeurIPS 2024Self-Labeling the Job Shop Scheduling Problem
- NeurIPS 2024Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation
- NeurIPS 2024Self-playing Adversarial Language Game Enhances LLM Reasoning
- NeurIPS 2024SelfCodeAlign: Self-Alignment for Code Generation
- NeurIPS 2024SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
- NeurIPS 2024Sequoia: Scalable and Robust Speculative Decoding
- NeurIPS 2024Simple and Effective Masked Diffusion 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。