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Spatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-learns the network parameters on different pseudo-classes simultaneously to create unbiased feature representation. Combining the learneddoi:10.1109/wacv.2017.118 dblp:conf/wacv/MunawarVM17 fatcat:atj54eyvjjc6dk4p6bp6upodxa