Understanding Contrastive Learning Requires Incorporating Inductive Biases

• URL: http://arxiv.org/abs/2202.14037v1
• Date: Mon, 28 Feb 2022 18:59:20 GMT
• Title: Understanding Contrastive Learning Requires Incorporating Inductive Biases
• Authors: Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
• Abstract summary: Recent attempts to theoretically explain the success of contrastive learning on downstream tasks prove guarantees depending on properties of em augmentations and the value of em contrastive loss of representations. We demonstrate that such analyses ignore em inductive biases of the function class and training algorithm, even em provably leading to vacuous guarantees in some settings.
• Score: 64.56006519908213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmentations of different inputs. Recent attempts to theoretically explain the success of contrastive learning on downstream classification tasks prove guarantees depending on properties of {\em augmentations} and the value of {\em contrastive loss} of representations. We demonstrate that such analyses, that ignore {\em inductive biases} of the function class and training algorithm, cannot adequately explain the success of contrastive learning, even {\em provably} leading to vacuous guarantees in some settings. Extensive experiments on image and text domains highlight the ubiquity of this problem -- different function classes and algorithms behave very differently on downstream tasks, despite having the same augmentations and contrastive losses. Theoretical analysis is presented for the class of linear representations, where incorporating inductive biases of the function class allows contrastive learning to work with less stringent conditions compared to prior analyses.

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