SSII2021 [OS2-03] 自己教師あり学習における対照学習の基礎と応用
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SSII2021 [OS2-03] 自己教師あり学習における対照学習の基礎と応用 6月10日 (木) 11:00 - 12:30 メイン会場（vimeo + sli.do） 登壇者：Xueting Wang 氏 （株式会社サイバーエージェント） 概要：自己教師あり学習は、データ自身から用意した教師を用いてデータの表現を学習することで、人間のアノテーション作業の負担を大きく削減可能という高い実用性を持つ。その中でも、特に近年では対照学習(Contrastive Learning)と呼ばれる技術は、教師あり学習に匹敵、あるいはそれ以上の性能を示すことで注目されている。本講演では、講演者の研究事例に基づき、対照学習の原理や応用について紹介する。
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SSII2021 [OS2-03] 自己教師あり学習における対照学習の基礎と応用
- 1. SSII2021 自己教師あり学習における 対照学習の基礎と応用 2021.6.10 汪 雪婷 Wang Xueting（CyberAgent, Inc.） 1 Contrastive Self-Supervised Learning
- 2. Self-Supervised Learning Pretrain-Finetune Pipeline SSII2021 Representation classifier Cat Tiger Label Representation classifier Dog Supervised Pipeline Pretrain Finetune (downstream tasks) • Large amount of data • High annotation cost 2
- 3. Self-Supervised Learning Pretrain-Finetune Pipeline 3 SSII2021 Representation classifier Cat Tiger Label Representation classifier Dog Representation Pretext Task Representation classifier Dog Supervised Pipeline Self-Supervised Pipeline No Annotated Label ! Pretrain Finetune (downstream tasks)
- 4. Self-Supervised Learning Pretrain-Finetune Pipeline 4 SSII2021 Representation classifier Cat Tiger Label Representation classifier Dog Representation Pretext Task Representation classifier Dog Supervised Pipeline Self-Supervised Pipeline No Annotated Label ! Pretrain Finetune Traditional supervised learning • Large amount of data+annotated labels • Task-specific learning, limited generalization Self-supervised learning • Reducing cost of human annotations • Supervision from the data itself • General representation learning • Alternative downstream tasks
- 5. Self-Supervised Learning Pretrain-Finetune Pipeline 5 SSII2021 Representation Pretext Task Representation classifier Dog Self-Supervised Pipeline No Annotated Label ! Point • How to learn effective representation from unlabeled data? • How to design effective pretext from the data itself? Self-supervised learning • Reducing cost of human annotations • Supervision from the data itself • General representation learning • Alternative downstream tasks
- 6. Self-Supervised Learning How : Paradigm Overview 6 SSII2021 Representation Similar or not Generative / Predictive Contrastive Representation • Loss measured in the output space • Learning to reconstruct/predict Better reconstruction/prediction better representation • Loss measured in the representation space • Learning to distinguish Do not need to reconstruct all detail focus on distinguishing samples Positive Negative Anchor Original Generated Eg. Auto-Encoders, BERT
- 7. Self-Supervised Learning How : Paradigm Overview SSII2021 Generative / Predictive Representation • Loss measured in the output space • Learning to reconstruct/predict Better reconstruction/prediction better representation Original Generated Eg. Auto-Encoders, BERT Top: Drawing of a dollar bill from memory Down: Drawing subsequently made with a dollar bill present. [Image source: Epstein, 2016]
- 8. Self-Supervised Learning How : Paradigm Overview 8 SSII2021 Representation Similar or not Generative / Predictive Contrastive • Loss measured in the output space • Learning to reconstruct/predict Better reconstruction/prediction better representation • Loss measured in the representation space • Learning to distinguish Do not need to reconstruct all detail focus on distinguishing samples Positive Negative Anchor Representation Original Generated
- 9. Contrastive Learning Point: distinguish features among different instances 9 SSII2021 Representation Positive (𝒙+ ) Negative (𝐱𝐣, 𝒕𝒉𝒆 𝒐𝒕𝒉𝒆𝒓 𝒔𝒂𝒎𝒑𝒍𝒆𝒔) Anchor (𝒙) Positive Anchor Negative Anchor similar dissimilar InfoNCE Loss [Gutmann+, AISTATS’10]
- 10. Contrastive Learning • MoCo: Momentum contrast for unsupervised visual representation learning [CVPR’20] • SimCLR: A Simple Framework for Contrastive Learning of Visual Representations [ICML’20] SSII2021 10 Recent related works Increase negatives Increase positives
- 11. MoCo[CVPR’20] Points (Negative samples) He, Kaiming, et al. "Momentum contrast for unsupervised visual representation learning." CVPR2020. SSII2021 11
- 12. MoCo[CVPR’20] He, Kaiming, et al. "Momentum contrast for unsupervised visual representation learning." CVPR2020. SSII2021 12 End-to-end: • Negative • All the other samples (-anchor/positive) • Two encoders • q: anchor; k:positive/negative • Benefit from large batch size • Memory problem
- 13. MoCo[CVPR’20] He, Kaiming, et al. "Momentum contrast for unsupervised visual representation learning." CVPR2020. SSII2021 13 Memory Bank(MB): • Negative: • Embedding stored in MB • Random sampling from MB • Memory bank updating • Computing cost problem
- 14. MoCo[CVPR’20] He, Kaiming, et al. "Momentum contrast for unsupervised visual representation learning." CVPR2020. SSII2021 14 Momentum encoder • Encoder: • Only positive sample • Negative: • Past embeddings of positives • Queue: save embedded features • Updating: weight of momentum encoder
- 15. SimCLR[ICML’20] Points • Positive samples: data augmentation • Random crops + color distortion • Negative samples: larger batch size(end-to-end) SimCLR:Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." ICML2020. SSII2021 15
- 16. Effect of Recent Works SSII2021 16 source: [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations] • Performance approaching supervised methods • Limitation: more time & parameters • Less labeling cost • High generalization (Eg. cross-domain)
- 17. Summary of Recent Works • Positive samples: • Multi-sampling method: transformation; crops; • Negative samples: • Larger batch size • Save previous features: memory bank ; queue SSII2021 17 • Intra-positives: same instance, same class • Inter-negatives: different instances
- 18. Inter-intra Contrastive Framework [MM’20] SSII2021 18 • Intra-positives: same instance, same class Intra-negatives: same instance, different class • Inter-positives: different instances, same class Inter-negatives: different instances Traditional contrastive learning IIC: L. TAO, X. Wang, and T. YAMASAKI, “Self-supervised Video Representation Learning Using Inter- intra Contrastive Framework”, ACMMM2020. Inter-intra contrastive (IIC) learning framework makes the most use of data
- 19. Proposed Concept (video task) Constrains of our method • Intra-positive: multi view (CMC based) • Optical flow • Frame difference (Residual frame) • Inter-negative: • Different instances • Intra-negative samples • Same instance destroying temporal information 19 Inter-intra contrastive learning SSII2021 CMC: Tian, Yonglong et al. “Contrastive Multiview Coding.”, ArXiv abs/1906.05849 (2019): n. pag.
- 20. Generation of Intra-negative Samples • Break temporal relations • Similar statistical information • Two options • Frame repeating • Frame shuffling 20 SSII2021
- 21. Evaluation Downstream tasks •Video retrieval •Video recognition 21 SSII2021
- 22. Results: video retrieval • On UCF101 22 SSII2021 • On HMDB51
- 23. Results: video recognition • SSL pretrained on • UCF101 split 1 • Finetuned on • UCF101 (3 splits) • HMDB51 (3 splits) 23 * indicates results using the same network backbone, R3D. SSII2021
- 24. Summary of IIC • Introduce intra-negative samples to encourage models to learn rich temporal information • Significant improvements over the state-of-the-art methods are achieved on two video tasks 24 Project page GitHub repo SSII2021
- 25. Directions of Contrastive Self-supervised Learning • Pretext task selection/design • Pair samples • Find and use effective positive/negative samples • Combine with supervised learning • Supervised contrastive learning • Task related Self-supervised Learning SSII2021 25
- 26. Thank you for your attention! SSII2021 26