AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent Loss

17 Sep 2020 Yanlun Tu Jianxing Feng Yang Yang

Self-supervised representation learning is an emerging research topic for its powerful capacity in learning with unlabeled data. As a mainstream self-supervised learning method, augmentation-based contrastive learning has achieved great success in various computer vision tasks that lack manual annotations... (read more)

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