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Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies
[article]
2022
arXiv
pre-print
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed "structural" peculiarity on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual signals. In this paper, however, we argue that learning such structure-sensitive representations can be a
arXiv:2109.10135v2
fatcat:y4jghafve5ghfo7ll5r6ozczii