• Corpus ID: 252918149

Self-supervision is not magic: Understanding Data Augmentation in Image Anomaly Detection

  title={Self-supervision is not magic: Understanding Data Augmentation in Image Anomaly Detection},
  author={Jaemin Yoo and Tianchen Zhao and Leman Akoglu},
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world tasks, avoiding the extensive cost of labeling. SSL is particularly attractive for unsupervised tasks such as anomaly detection (AD), where labeled anomalies are costly to secure, difficult to simulate, or even nonexistent. A large catalog of augmentation functions have been used for SSL-based AD (SSAD) on image data, and recent works have observed that the type of augmentation has a… 



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