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 4 / 5 页
- NeurIPS 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation
- NeurIPS 2025Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
- NeurIPS 2025PID-controlled Langevin Dynamics for Faster Sampling on Generative Models
- NeurIPS 2025Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
- NeurIPS 2025Plug-and-Play Context Feature Reuse for Efficient Masked Generation
- NeurIPS 2025Position: Benchmarking is Broken - Don't Let AI be Its Own Judge
- NeurIPS 2025Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism
- NeurIPS 2025Preconditioned Langevin Dynamics with Score-based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
- NeurIPS 2025Principled Long-Tailed Generative Modeling via Diffusion Models
- NeurIPS 2025Probing Equivariance and Symmetry Breaking in Convolutional Networks
- NeurIPS 2025Prohibiting Generative AI in any Form of Weapon Control
- NeurIPS 2025Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
- NeurIPS 2025Random Forest Autoencoders for Guided Representation Learning
- NeurIPS 2025Real-Time Hyper-Personalized Generative AI Should Be Regulated to Prevent the Rise of "Digital Heroin"
- NeurIPS 2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
- NeurIPS 2025ReDi: Rectified Discrete Flow
- NeurIPS 2025REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training
- NeurIPS 2025RETRO SYNFLOW: Discrete Flow-Matching for Accurate and Diverse Single-Step Retrosynthesis
- NeurIPS 2025Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws
- NeurIPS 2025RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
- NeurIPS 2025RGB-to-Polarization Estimation: A New Task and Benchmark Study
- NeurIPS 2025Riemannian Consistency Model
- NeurIPS 2025Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
- NeurIPS 2025Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments
- NeurIPS 2025RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models
- NeurIPS 2025ROGR: Relightable 3D Objects using Generative Relighting
- NeurIPS 2025Rotary Masked Autoencoders are Versatile Learners
- NeurIPS 2025SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
- NeurIPS 2025Salient Concept-Aware Generative Data Augmentation
- NeurIPS 2025Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
- NeurIPS 2025Sample-Efficient Multi-Round Generative Data Augmentation for Long-Tail Instance Segmentation
- NeurIPS 2025Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
- NeurIPS 2025Scalable In-context Ranking with Generative Models
- NeurIPS 2025Scaling Offline RL via Efficient and Expressive Shortcut Models
- NeurIPS 2025Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres
- NeurIPS 2025SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories
- NeurIPS 2025Seeds of Structure: Patch PCA Reveals Universal Compositional Cues in Diffusion Models
- NeurIPS 2025Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
- NeurIPS 2025SGAR: Structural Generative Augmentation for 3D Human Motion Retrieval
- NeurIPS 2025Shape-Informed Clustering of Multi-Dimensional Functional Data via Deep Functional Autoencoders
- NeurIPS 2025ShapeEmbed: a self-supervised learning framework for 2D contour quantification
- NeurIPS 2025Shortcut Features as Top Eigenfunctions of NTK: A Linear Neural Network Case and More
- NeurIPS 2025Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression
- NeurIPS 2025Smooth and Flexible Camera Movement Synthesis via Temporal Masked Generative Modeling
- NeurIPS 2025SOMBRL: Scalable and Optimistic Model-Based RL
- NeurIPS 2025Space Group Equivariant Crystal Diffusion
- NeurIPS 2025SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
- NeurIPS 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
- NeurIPS 2025SpecEM: Training-Free LLM Ensembling via Iterative Drafting, Verification, and Online Feedback
- NeurIPS 2025Spectral Conditioning of Attention Improves Transformer Performance
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。