The Authors provide a bag of tricks for training GAN's in the image domain. Using these, they achieve very strong semi-supervised results on SHVN, MNIST, and CIFAR. The authors then train the improved model on several images datasets, evaluate it on different tasks: semi-supervised learning, and generative capabilities, and achieve state-of-the-art results. This paper investigates several techniques to stabilize GAN training and encourage convergence. Although lack of theoretical justification, the proposed heuristic techniques give better-looking samples. In addition to human judgement, the paper proposes a new metric called Inception score by applying pre-trained deep classification network on the generated samples. By introducing free labels with the generated samples as new category, the paper proposes the experiment using GAN under semi-supervised learning setting, which achieve SOTA semi-supervised performance on several benchmark datasets (MNIST, CIFAR-10, and SVHN).