Translations:Generative Adversarial Nets/24/en

GANs sparked one of the most active areas of deep learning research. Within a few years of publication, thousands of GAN variants were proposed, addressing training instability (WGAN, spectral normalization), enabling conditional generation (cGAN, pix2pix), achieving photorealistic image synthesis (StyleGAN, BigGAN), and extending to video, 3D, and other modalities. The adversarial training principle was also applied to domain adaptation, data augmentation, super-resolution, and text-to-image generation.