This paper presents new architectural features and training procedures for generative adversarial networks (GANs), focusing on semi-supervised learning and image generation. The authors introduce several techniques to improve GAN training and achieve state-of-the-art results on MNIST, CIFAR-10, and SVHN for semi-supervised classification. Their generated images are visually realistic, with MNIST samples indistinguishable from real data and CIFAR-10 samples achieving a human error rate of 21.3%. They also generate high-resolution ImageNet samples, showing that their methods enable the model to learn recognizable features of ImageNet classes. The paper discusses several techniques for improving GAN training, including feature matching, minibatch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization. These techniques aim to stabilize GAN training and improve the quality of generated images. The authors also propose an evaluation metric, the Inception score, which correlates well with human judgment of image quality. In semi-supervised learning, the authors use GANs to generate additional unlabeled data, which is then used to improve the performance of a classifier. They show that their techniques improve the quality of generated images and the performance of the classifier. The results demonstrate that their methods are effective for semi-supervised learning and image generation, achieving state-of-the-art results on several datasets. The paper concludes that GANs are a promising class of generative models, but their training and evaluation remain challenging. The authors hope to develop a more rigorous theoretical understanding of GANs in future work.This paper presents new architectural features and training procedures for generative adversarial networks (GANs), focusing on semi-supervised learning and image generation. The authors introduce several techniques to improve GAN training and achieve state-of-the-art results on MNIST, CIFAR-10, and SVHN for semi-supervised classification. Their generated images are visually realistic, with MNIST samples indistinguishable from real data and CIFAR-10 samples achieving a human error rate of 21.3%. They also generate high-resolution ImageNet samples, showing that their methods enable the model to learn recognizable features of ImageNet classes. The paper discusses several techniques for improving GAN training, including feature matching, minibatch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization. These techniques aim to stabilize GAN training and improve the quality of generated images. The authors also propose an evaluation metric, the Inception score, which correlates well with human judgment of image quality. In semi-supervised learning, the authors use GANs to generate additional unlabeled data, which is then used to improve the performance of a classifier. They show that their techniques improve the quality of generated images and the performance of the classifier. The results demonstrate that their methods are effective for semi-supervised learning and image generation, achieving state-of-the-art results on several datasets. The paper concludes that GANs are a promising class of generative models, but their training and evaluation remain challenging. The authors hope to develop a more rigorous theoretical understanding of GANs in future work.