The paper introduces a novel training methodology for Generative Adversarial Networks (GANs) that involves progressive growth of both the generator and discriminator. This method starts with low-resolution images and gradually increases the resolution by adding new layers to the networks, which helps stabilize training and improve image quality. The authors also propose a method to increase the variation in generated images and introduce a new metric for evaluating GAN results. They demonstrate the effectiveness of their approach on datasets such as CELEBA, LSUN, and CIFAR10, achieving improved inception scores and higher-quality images. Additionally, they create a high-resolution version of the CELEBA dataset to allow for experimentation with output resolutions up to 1024x1024 pixels. The paper includes detailed descriptions of network structures, training configurations, and experimental results, highlighting the benefits of progressive growing in terms of convergence, stability, and image quality.The paper introduces a novel training methodology for Generative Adversarial Networks (GANs) that involves progressive growth of both the generator and discriminator. This method starts with low-resolution images and gradually increases the resolution by adding new layers to the networks, which helps stabilize training and improve image quality. The authors also propose a method to increase the variation in generated images and introduce a new metric for evaluating GAN results. They demonstrate the effectiveness of their approach on datasets such as CELEBA, LSUN, and CIFAR10, achieving improved inception scores and higher-quality images. Additionally, they create a high-resolution version of the CELEBA dataset to allow for experimentation with output resolutions up to 1024x1024 pixels. The paper includes detailed descriptions of network structures, training configurations, and experimental results, highlighting the benefits of progressive growing in terms of convergence, stability, and image quality.