Data Augmentation Guidelines for Cross-Dataset Transfer Learning and Pseudo Labeling

Convolutional Neural Networks require large amounts of labeled data in order to be trained. To improve such performances, a practical approach widely used is to augment the training set data, generating compatible data. Standard data augmentation for images includes conventional techniques, such as rotation, shift, and flip. In this paper, we go beyond such methods by studying alternative augmenta...