DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data
DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image …