Method of and system for joint data augmentation and classification learning

Abstract

A method and a system for joint data augmentation and classification learning, where an augmentation network learns to perform transformations and a classification network is trained. A set of labelled images is received. During an inner loop iteration, an augmentation network applies a transformation on a given labelled image of the set to obtain a transformed image. The classification network classifies the transformed image to obtain a predicted class, and a training loss is determined based on the predicted class and the respective label. The parameters of the classification network are updated based on the classification loss. During an outer loop iteration, the classification network classifies another labelled image of the set to obtain another predicted class, and a validation loss is determined based on the other predicted class and the respective label. The parameters of the augmentation network are updated based on the validation loss.