This paper presents several new architectural features and training procedures for Generative Adversarial Networks (GANs). The authors focus on two primary applications: semi-supervised learning and generating visually realistic images. Unlike previous work, their main goal is not to maximize the likelihood of test data but to improve the quality of generated images and enhance semi-supervised learning performance. They introduce techniques such as feature matching, minibatch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization to stabilize GAN training and improve convergence. These techniques lead to state-of-the-art results in semi-supervised classification on datasets like MNIST, CIFAR-10, and SVHN. The generated images are of high quality, as confirmed by a visual Turing test, where humans cannot distinguish between generated and real samples. The paper also demonstrates that the proposed methods enable the model to learn recognizable features of ImageNet classes, achieving unprecedented resolution. The authors hope that these techniques will form the basis for future work, providing formal guarantees of convergence.This paper presents several new architectural features and training procedures for Generative Adversarial Networks (GANs). The authors focus on two primary applications: semi-supervised learning and generating visually realistic images. Unlike previous work, their main goal is not to maximize the likelihood of test data but to improve the quality of generated images and enhance semi-supervised learning performance. They introduce techniques such as feature matching, minibatch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization to stabilize GAN training and improve convergence. These techniques lead to state-of-the-art results in semi-supervised classification on datasets like MNIST, CIFAR-10, and SVHN. The generated images are of high quality, as confirmed by a visual Turing test, where humans cannot distinguish between generated and real samples. The paper also demonstrates that the proposed methods enable the model to learn recognizable features of ImageNet classes, achieving unprecedented resolution. The authors hope that these techniques will form the basis for future work, providing formal guarantees of convergence.