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 7 / 7 页
- NeurIPS 2024Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
- NeurIPS 2024Taming Generative Diffusion Prior for Universal Blind Image Restoration
- NeurIPS 2024Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
- NeurIPS 2024Tetrahedron Splatting for 3D Generation
- NeurIPS 2024The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
- NeurIPS 2024The GAN is dead; long live the GAN! A Modern GAN Baseline
- NeurIPS 2024The tree autoencoder model, with application to hierarchical data visualization
- NeurIPS 2024Theoretical guarantees in KL for Diffusion Flow Matching
- NeurIPS 2024Thought of Search: Planning with Language Models Through The Lens of Efficiency
- NeurIPS 2024Time-Reversal Provides Unsupervised Feedback to LLMs
- NeurIPS 2024Towards a Scalable Reference-Free Evaluation of Generative Models
- NeurIPS 2024Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
- NeurIPS 2024Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
- NeurIPS 2024Training Data Attribution via Approximate Unrolling
- NeurIPS 2024TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks
- NeurIPS 2024Transcendence: Generative Models Can Outperform The Experts That Train Them
- NeurIPS 2024Transcoders find interpretable LLM feature circuits
- NeurIPS 2024Transferring disentangled representations: bridging the gap between synthetic and real images
- NeurIPS 2024Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
- NeurIPS 2024Transformer Doctor: Diagnosing and Treating Vision Transformers
- NeurIPS 2024Transformers Can Do Arithmetic with the Right Embeddings
- NeurIPS 2024Transformers on Markov data: Constant depth suffices
- NeurIPS 2024Truncated Variance Reduced Value Iteration
- NeurIPS 2024TSDS: Data Selection for Task-Specific Model Finetuning
- NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
- NeurIPS 2024Understanding Hallucinations in Diffusion Models through Mode Interpolation
- NeurIPS 2024Understanding Information Storage and Transfer in Multi-Modal Large Language Models
- NeurIPS 2024UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
- NeurIPS 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
- NeurIPS 2024Unity by Diversity: Improved Representation Learning for Multimodal VAEs
- NeurIPS 2024Universal In-Context Approximation By Prompting Fully Recurrent Models
- NeurIPS 2024Universal Sample Coding
- NeurIPS 2024Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
- NeurIPS 2024Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
- NeurIPS 2024Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
- NeurIPS 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
- NeurIPS 2024VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
- NeurIPS 2024Video Diffusion Models are Training-free Motion Interpreter and Controller
- NeurIPS 2024VideoTetris: Towards Compositional Text-to-Video Generation
- NeurIPS 2024Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels
- NeurIPS 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
- NeurIPS 2024Visual Data Diagnosis and Debiasing with Concept Graphs
- NeurIPS 2024Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
- NeurIPS 2024Visual Perception by Large Language Model’s Weights
- NeurIPS 2024Vivid-ZOO: Multi-View Video Generation with Diffusion Model
- NeurIPS 2024WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
- NeurIPS 2024Wings: Learning Multimodal LLMs without Text-only Forgetting
- NeurIPS 2024YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
- NeurIPS 2024ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification
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。