SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

• URL: http://arxiv.org/abs/2106.15147v1
• Date: Tue, 29 Jun 2021 08:08:33 GMT
• Title: SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
• Authors: Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
• Abstract summary: We propose SCARF, a technique for contrastive learning, where views are formed by corrupting a random subset of features. We show that SCARF complements existing strategies and outperforms alternatives like autoencoders.
• Score: 72.35532598131176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data. However, such methods are domain-specific and little has been done to leverage this technique on real-world tabular datasets. We propose SCARF, a simple, widely-applicable technique for contrastive learning, where views are formed by corrupting a random subset of features. When applied to pre-train deep neural networks on the 69 real-world, tabular classification datasets from the OpenML-CC18 benchmark, SCARF not only improves classification accuracy in the fully-supervised setting but does so also in the presence of label noise and in the semi-supervised setting where only a fraction of the available training data is labeled. We show that SCARF complements existing strategies and outperforms alternatives like autoencoders. We conduct comprehensive ablations, detailing the importance of a range of factors.

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