Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look

• URL: http://arxiv.org/abs/2410.12396v1
• Date: Wed, 16 Oct 2024 09:25:11 GMT
• Title: Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look
• Authors: Yong Zhang, Rui Zhu, Shifeng Zhang, Xu Zhou, Shifeng Chen, Xiaofan Chen,
• Abstract summary: We propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity.
• Score: 28.350278251132078
• License:
• Abstract: Self-supervised contrastive learning heavily relies on the view variance brought by data augmentation, so that it can learn a view-invariant pre-trained representation. Beyond increasing the view variance for contrast, this work focuses on improving the diversity of training data, to improve the generalization and robustness of the pre-trained models. To this end, we propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity. We perform a systematic investigation of various feature augmentation architectures, the gradient-flow skill, and the relationship between feature augmentation and traditional data augmentation. Our study reveals some practical principles for feature augmentation in self-contrastive learning. By integrating feature augmentation on the instance discrimination or the instance similarity paradigm, we consistently improve the performance of pre-trained feature learning and gain better generalization over the downstream image classification and object detection task.

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