Adversarial Contrastive Self-Supervised Learning

• URL: http://arxiv.org/abs/2202.13072v1
• Date: Sat, 26 Feb 2022 05:57:45 GMT
• Title: Adversarial Contrastive Self-Supervised Learning
• Authors: Wentao Zhu, Hang Shang, Tingxun Lv, Chao Liao, Sen Yang, Ji Liu
• Abstract summary: We present a novel self-supervised deep learning paradigm based on online hard negative pair mining. We derive a new triplet-like loss considering both positive sample pairs and mined hard negative sample pairs.
• Score: 13.534367890379853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention. Self-supervised learning followed by the supervised fine-tuning on a few labeled examples can significantly improve label efficiency and outperform standard supervised training using fully annotated data. In this work, we present a novel self-supervised deep learning paradigm based on online hard negative pair mining. Specifically, we design a student-teacher network to generate multi-view of the data for self-supervised learning and integrate hard negative pair mining into the training. Then we derive a new triplet-like loss considering both positive sample pairs and mined hard negative sample pairs. Extensive experiments demonstrate the effectiveness of the proposed method and its components on ILSVRC-2012.

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