using unlabeled data which is much more abundant and and easy cheaper to accumulate and data augmentation which basically stretches your supervised labeled samples further and as well are we good with the sound data augmentation has mostly been applied in the supervised setting and so we want to see if it can be applied in the unsupervised setting as well the main contributions which we'll get into are applying state-of-the-art data augmentation to semi-supervised learning a training technique called TSA training SiC leg