Transcription of CutPaste: Self-Supervised Learning for Anomaly Detection ...
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CutPaste: Self-Supervised Learning for Anomaly Detection ...
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationChun-Liang Li , Kihyuk Sohn , Jinsung Yoon, Tomas PfisterGoogle Cloud AI aim at constructing a high performance model for de-fect Detection that detects unknown anomalous patterns ofan image without anomalous data. To this end, we proposea two-stage framework for building Anomaly detectors us-ing normal training data only. We first learn self -superviseddeep representations and then build a generative one-classclassifier on learned representations. We learn representa-tions by classifying normal data from the CutPaste, a sim-ple data augmentation strategy that cuts an image patch andpastes at a random location of a large image.
2.1. Self-Supervised Learning with CutPaste Defining good pretext tasks is essential for self-supervised representation learning. While popular meth-ods including rotation prediction [19] and contrastive learn-ing [60, 12] have been studied in the context of semantic one-class classification [20, 24, 4, 54, 52], our study in Sec-
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