2016
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
NeurIPS 2016poster
Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data availab…