A New Google AI Research Study Discovers Anomalous Data Using Self Supervised Learning

One-class classification is beneficial for anomaly detection. It determines whether an instance belongs to the same distribution as the training data by assuming that the training data are all normal examples. However, representation learning is not available to these old methods. Furthermore, self-supervised learning has made significant progress in learning visual representations from unlabeled data, including rotation prediction and contrastive learning. 

Continue Reading..