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 1,216 篇，第 12 / 25 页
- NeurIPS 2023Characterizing Out-of-Distribution Error via Optimal Transport
- NeurIPS 2023Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models
- NeurIPS 2023Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark
- NeurIPS 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
- NeurIPS 2023ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
- NeurIPS 2023Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits
- NeurIPS 2023Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
- NeurIPS 2023Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
- NeurIPS 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
- NeurIPS 2023Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
- NeurIPS 2023Collaborative Learning via Prediction Consensus
- NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
- NeurIPS 2023CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
- NeurIPS 2023Combinatorial Group Testing with Selfish Agents
- NeurIPS 2023Combining Behaviors with the Successor Features Keyboard
- NeurIPS 2023Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems
- NeurIPS 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
- NeurIPS 2023Complexity Matters: Rethinking the Latent Space for Generative Modeling
- NeurIPS 2023Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
- NeurIPS 2023Compositional Generalization from First Principles
- NeurIPS 2023Compositional Sculpting of Iterative Generative Processes
- NeurIPS 2023Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
- NeurIPS 2023Concept Algebra for (Score-Based) Text-Controlled Generative Models
- NeurIPS 2023Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
- NeurIPS 2023Conditional independence testing under misspecified inductive biases
- NeurIPS 2023Conditional score-based diffusion models for Bayesian inference in infinite dimensions
- NeurIPS 2023Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
- NeurIPS 2023Connecting Pre-trained Language Model and Downstream Task via Properties of Representation
- NeurIPS 2023Conservative State Value Estimation for Offline Reinforcement Learning
- NeurIPS 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
- NeurIPS 2023Contextual Bandits and Imitation Learning with Preference-Based Active Queries
- NeurIPS 2023Contextually Affinitive Neighborhood Refinery for Deep Clustering
- NeurIPS 2023Continuous-Time Functional Diffusion Processes
- NeurIPS 2023Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time
- NeurIPS 2023Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
- NeurIPS 2023Contrastive Sampling Chains in Diffusion Models
- NeurIPS 2023Contrastive Training of Complex-Valued Autoencoders for Object Discovery
- NeurIPS 2023Convolutional Neural Operators for robust and accurate learning of PDEs
- NeurIPS 2023Coop: Memory is not a Commodity
- NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
- NeurIPS 2023Counterfactual Generation with Identifiability Guarantees
- NeurIPS 2023Counterfactually Fair Representation
- NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
- NeurIPS 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
- NeurIPS 2023CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
- NeurIPS 2023CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation
- NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
- NeurIPS 2023Curvature Filtrations for Graph Generative Model Evaluation
- NeurIPS 2023Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
- NeurIPS 2023DAC-DETR: Divide the Attention Layers and Conquer
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。