text: 129,18,1410039972422328322,1250475209959698432,Dara Bahri,"Excited to share our new paper from @GoogleAI. ""SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption"" (<LINK>). This is joint work with Heinrich Jiang, @ytay017 and @metzlerd. <LINK> TLDR -- a neat contrastive pre-training scheme for deep nets on tabular data that improves classification performance generally, when labeled data is limited, and when there are noisy labels.",https://arxiv.org/abs/2106.15147,"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. ","SCARF: Self-Supervised Contrastive Learning using Random Feature