Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies
Taeyeong Choi,Owen Would,Adrian Salazar-Gomez,Grzegorz Cielniak,Taeyeong Choi,Owen Would,Adrian Salazar-Gomez,Grzegorz Cielniak
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for self-supervised 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 signa...
