Gradient-based Data Augmentation for Semi-Supervised Learning
Gradient-based Data Augmentation for Semi-Supervised Learning
In semi-supervised learning (SSL), a technique called consistency regularization (CR) achieves high performance. It has been proved that the diversity of data used in CR is extremely important to obtain a model with high discrimination performance by CR. We propose a new data augmentation (Gradient-based Data Augmentation (GDA)) that is …