The projects aims to tackle the problem of data scarcity for training data-hungry neural networks using the semi-supervised learning which relies on small amount of labelled dataset. Three techniques namely - Pseudo-labeling, Virtual Adversarial Training (VAT) and Attention-based methods have been implemented and analyzed on CIFAR-10 and CIFAR-100 datasets.
Checkout Problem Statement and Report for more details.