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

ICML/NeurIPS 深度学习方向:self_supervised_learning

统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 1,958 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

深度学习 · self supervised learning论文数,按年份和会议
年份NeurIPSICLRICMLCVPR合计
202316010841146455
202411019562105472
20251471957283497
202611019513594534

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

全部深度学习 · self supervised learning论文

共 135 篇,第 1 / 3 页

  1. ICML 2026$f$-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models
  2. ICML 20263D-DLP: Self-supervised 3D Object-centric Scene Representation Learning
  3. ICML 20263DPoV: Improving 3D understanding via Patch Ordering on Videos
  4. ICML 2026A Random Matrix Theory of Masked Self-Supervised Learning
  5. ICML 2026A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization
  6. ICML 2026Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment
  7. ICML 2026ArcDAE: Asymmetric Rectified Contrastive Diffusion Autoencoder for Unified Representation Learning
  8. ICML 2026AudioMosaic: Contrastive Masked Audio Representation Learning
  9. ICML 2026Autoregression with Self-Token Prediction
  10. ICML 2026BAT: Better Audio Transformer Guided by Convex Gated Probing
  11. ICML 2026Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
  12. ICML 2026Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
  13. ICML 2026Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
  14. ICML 2026Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data
  15. ICML 2026Breaking the Reference Bottleneck via Learning to Rewrite Conversational Queries without Gold Reference Passages
  16. ICML 2026Brep2Shape: Boundary and Shape Representation Alignment via Self-supervised Transformers
  17. ICML 2026CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
  18. ICML 2026Can local learning match self-supervised backpropagation?
  19. ICML 2026Causal-JEPA: Learning World Models through Object-Level Latent Masking
  20. ICML 2026Certifying Graph Neural Networks Against Label and Structure Poisoning
  21. ICML 2026CodeMamba: Shifting from Target Semantics to Self-Supervised Background Manifold Learning for Singularity Detection in Infrared Sequences
  22. ICML 2026Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
  23. ICML 2026Deriving Neural Scaling Laws from the Statistics of Natural Language
  24. ICML 2026DiL: Discrete-anchored Representation Alignment for Semi-Supervised Continual Learning
  25. ICML 2026Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
  26. ICML 2026Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
  27. ICML 2026DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation
  28. ICML 2026DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement
  29. ICML 2026Divide and Contrast: Learning Robust Temporal Features without Augmentation
  30. ICML 2026DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
  31. ICML 2026Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
  32. ICML 2026FairSSL: Fair Multimodal Self-Supervised Learning
  33. ICML 2026Faster Query-Key Learning Sharpens Attention in Self-Attention Models
  34. ICML 2026FedSDR: Federated Self-Distillation with Rectification
  35. ICML 2026FedVeer: Self-Adaptive Skew Estimation for Robust Federated Learning
  36. ICML 2026Fine-Tuning Masked Diffusion for Provable Self-Correction
  37. ICML 2026Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
  38. ICML 2026FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
  39. ICML 2026From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
  40. ICML 2026Gauge-Equivariant Graph Networks via Self-Interference Cancellation
  41. ICML 2026Generalized Discrete Diffusion with Self-Correction
  42. ICML 2026Geometric Collapse: When Vision Models Fail to Verify Physical Causality
  43. ICML 2026Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
  44. ICML 2026GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
  45. ICML 2026Global Geometry Is Not Enough for Vision Representations
  46. ICML 2026Hierarchical Goal Abstractions via Learned Subset Relations
  47. ICML 2026IdEst: Assessing Self-Supervised Learning Representations via Intrinsic Dimension
  48. ICML 2026Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR
  49. ICML 2026Improving Diffusion Planners by Self-Supervised Action Gating with Energies
  50. ICML 2026Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation

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 · self supervised learning在 ICLR 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-self-supervised-learning"}

两分钟接入 Paperlist MCP(Claude Code、Cursor、Codex、ChatGPT)

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。

「深度学习」下的其他主题