Generative adversarial networks
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We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature… Expand In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range… Expand One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we… Expand Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the… Expand We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can… Expand While humans easily recognize relations between data from different domains without any supervision, learning to automatically… Expand In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications… Expand We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast… Expand This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn… Expand Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this… Expand