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 7 / 16 页
- ICML 2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
- ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
- ICML 2024COPAL: Continual Pruning in Large Language Generative Models
- ICML 2024Deep Stochastic Mechanics
- ICML 2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
- ICML 2024Demystifying SGD with Doubly Stochastic Gradients
- ICML 2024DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
- ICML 2024Differentially Private Sum-Product Networks
- ICML 2024Diffusion Language Models Are Versatile Protein Learners
- ICML 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
- ICML 2024Disguised Copyright Infringement of Latent Diffusion Models
- ICML 2024Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
- ICML 2024Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
- ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
- ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
- ICML 2024Evaluating Model Bias Requires Characterizing its Mistakes
- ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
- ICML 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
- ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
- ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller
- ICML 2024FlowMM: Generating Materials with Riemannian Flow Matching
- ICML 2024From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers
- ICML 2024GenCO: Generating Diverse Designs with Combinatorial Constraints
- ICML 2024Generative Active Learning for Long-tailed Instance Segmentation
- ICML 2024Generative Conditional Distributions by Neural (Entropic) Optimal Transport
- ICML 2024Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
- ICML 2024Generative Marginalization Models
- ICML 2024Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
- ICML 2024Genie: Generative Interactive Environments
- ICML 2024GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation
- ICML 2024GiLOT: Interpreting Generative Language Models via Optimal Transport
- ICML 2024Graph Geometry-Preserving Autoencoders
- ICML 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
- ICML 2024HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
- ICML 2024High-Performance Temporal Reversible Spiking Neural Networks with $\mathcal{O}(L)$ Training Memory and $\mathcal{O}(1)$ Inference Cost
- ICML 2024How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
- ICML 2024How to Trace Latent Generative Model Generated Images without Artificial Watermark?
- ICML 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
- ICML 2024Image Hijacks: Adversarial Images can Control Generative Models at Runtime
- ICML 2024Imitation Learning from Purified Demonstrations
- ICML 2024Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
- ICML 2024Indirectly Parameterized Concrete Autoencoders
- ICML 2024Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
- ICML 2024Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
- ICML 2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
- ICML 2024Language Models as Semantic Indexers
- ICML 2024LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
- ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
- ICML 2024Learning to Infer Generative Template Programs for Visual Concepts
- ICML 2024Learning to Model the World With Language
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。