Deep Tabular Learning
SCARF
Introduced by Bahri et al. in SCARF: Self-Supervised Contrastive Learning using Random Feature CorruptionSCARF is 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.
Source: SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
Papers
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Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
