Self-supervised Label Augmentation via Input Transformations

ICLR 2020 Hankook LeeSung Ju HwangJinwoo Shin

Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with unlabeled datasets, i.e., learning without any human-annotated supervision. In this paper, we show that such a technique can be used to significantly improve the model accuracy even under fully-labeled datasets... (read more)

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