深度学习 · self supervised learning论文趋势 2023–2026

主要结论

• 深度学习 · self supervised learning论文从 2023 年的 455 篇增长到 2026 年的 534 篇，三年间扩大到约 1.2 倍。
• ICLR 是发深度学习 · self supervised learning论文最多的会议，2023–2026 年共 693 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是通用机器学习 · unsupervised and semisupervised learning：同时属于两个主题的论文从 2023 年的 23 篇增至 2026 年的 86 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 4；同组增长最快的扩散模型是 4.1 倍，深度学习 · self supervised learning是 1.2 倍。

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 2026Attending on Attention ($A^2$): Smaller Self-Supervised ViTs Localize Better Than Larger Ones
• ICML 20263D-DLP: Self-supervised 3D Object-centric Scene Representation Learning
• ICLR 2026A Single Architecture for Representing Invariance Under Any Space Group
• CVPR 2026A Stitch in Time Learning Procedural Workflow via Self-Supervised Plackett-Luce
• NeurIPS 2025$\textit{HiMaCon:}$ Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data
• ICML 2025An Augmentation-Aware Theory for Self-Supervised Contrastive Learning
• ICLR 2025Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding
• CVPR 2025A Unified Framework for Heterogeneous Semi-supervised Learning
• NeurIPS 2024Intrinsic Self-Supervision for Data Quality Audits
• ICML 2024Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios
• NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
• ICML 2023A Closer Look at Self-Supervised Lightweight Vision Transformers
• ICLR 2023A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification
• CVPR 2023Are Data-Driven Explanations Robust Against Out-of-Distribution Data?
• NeurIPS 2026AudioSphere: Towards Self-Supervised Spatial Audio Representation Models
• ICML 20263DPoV: Improving 3D understanding via Patch Ordering on Videos
• ICLR 2026Angle K-Means
• CVPR 2026Action Motifs Self-Supervised Hierarchical Representation of Human Body Movements

全部深度学习 · self supervised learning论文

共 527 篇，第 8 / 11 页

351. NeurIPS 2024SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
352. NeurIPS 2024Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks
353. NeurIPS 2024Supervised Kernel Thinning
354. NeurIPS 2024TAIA: Large Language Models are Out-of-Distribution Data Learners
355. NeurIPS 2024The Benefits of Balance: From Information Projections to Variance Reduction
356. NeurIPS 2024Theoretical Analysis of Weak-to-Strong Generalization
357. NeurIPS 2024Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution
358. NeurIPS 2024Towards a theory of how the structure of language is acquired by deep neural networks
359. NeurIPS 2024Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
360. NeurIPS 2024Uncovering the Redundancy in Graph Self-supervised Learning Models
361. NeurIPS 2024Understanding the Gains from Repeated Self-Distillation
362. NeurIPS 2024Understanding the Role of Equivariance in Self-supervised Learning
363. NeurIPS 2024Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs
364. NeurIPS 2024Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis
365. NeurIPS 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
366. NeurIPS 2024Where does In-context Learning Happen in Large Language Models?
367. NeurIPS 2024You Don’t Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
368. NeurIPS 2023$\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
369. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
370. NeurIPS 2023A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
371. NeurIPS 2023A polar prediction model for learning to represent visual transformations
372. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
373. NeurIPS 2023AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
374. NeurIPS 2023Alignment with human representations supports robust few-shot learning
375. NeurIPS 2023ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
376. NeurIPS 2023An Information Theory Perspective on Variance-Invariance-Covariance Regularization
377. NeurIPS 2023An Iterative Self-Learning Framework for Medical Domain Generalization
378. NeurIPS 2023Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
379. NeurIPS 2023Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
380. NeurIPS 2023BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
381. NeurIPS 2023Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
382. NeurIPS 2023Better Correlation and Robustness: A Distribution-Balanced Self-Supervised Learning Framework for Automatic Dialogue Evaluation
383. NeurIPS 2023Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
384. NeurIPS 2023Binary Classification with Confidence Difference
385. NeurIPS 2023Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
386. NeurIPS 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
387. NeurIPS 2023Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
388. NeurIPS 2023CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
389. NeurIPS 2023Can semi-supervised learning use all the data effectively? A lower bound perspective
390. NeurIPS 2023CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
391. NeurIPS 2023Characterizing the Impacts of Semi-supervised Learning for Weak Supervision
392. NeurIPS 2023Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
393. NeurIPS 2023Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
394. NeurIPS 2023Combating Representation Learning Disparity with Geometric Harmonization
395. NeurIPS 2023Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
396. NeurIPS 2023Contextually Affinitive Neighborhood Refinery for Deep Clustering
397. NeurIPS 2023Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
398. NeurIPS 2023Convolutional Visual Prompt for Robust Visual Perception
399. NeurIPS 2023Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
400. NeurIPS 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · self supervised learning论文？

共 1,958 篇，按年份分别是 2023 年 455 篇、2024 年 472 篇、2025 年 497 篇、2026 年 534 篇。

NeurIPS 2026 有多少篇深度学习 · self supervised learning论文？

NeurIPS 2026 有 110 篇深度学习 · self supervised learning论文，上一届是 147 篇。

ICLR 2026 有多少篇深度学习 · self supervised learning论文？

ICLR 2026 有 195 篇深度学习 · self supervised learning论文，上一届是 195 篇。

ICML 2026 有多少篇深度学习 · self supervised learning论文？

ICML 2026 有 135 篇深度学习 · self supervised learning论文，上一届是 72 篇。

CVPR 2026 有多少篇深度学习 · self supervised learning论文？

CVPR 2026 有 94 篇深度学习 · self supervised learning论文，上一届是 83 篇。

哪个会议的深度学习 · self supervised learning论文最多？

ICLR，2023–2026 年共 693 篇。