- Keywords: Self-supervised Learning, Unsupervised Learning, Multi-view Representation Learning
- Abstract: As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as object detection and image captioning. Many proposed approaches for self-supervised learning follow naturally a multi-view perspective, where the input (e.g., original images) and the self-supervised signals (e.g., augmented images) can be seen as two redundant views of the data. Building from this multi-view perspective, this paper provides an information-theoretical framework to better understand the properties that encourage successful self-supervised learning. Specifically, we demonstrate that self-supervised learned representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design. In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information. To verify our analysis, we conduct controlled experiments to evaluate the impact of the composite objectives. We also explore our framework's empirical generalization beyond the multi-view perspective, where the cross-view redundancy may not be clearly observed.
- One-sentence Summary: From a multi-view learning perspective, this paper provides both theoretical and empirical analysis on self-supervised learning.
- Supplementary Material: zip
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- Code: [![github](/images/github_icon.svg) yaohungt/Demystifying_Self_Supervised_Learning](https://github.com/yaohungt/Demystifying_Self_Supervised_Learning)
- Data: [COCO](https://paperswithcode.com/dataset/coco)