  In this paper we introduce Curriculum GANs, a curriculum learning strategy
for training Generative Adversarial Networks that increases the strength of the
discriminator over the course of training, thereby making the learning task
progressively more difficult for the generator. We demonstrate that this
strategy is key to obtaining state-of-the-art results in image generation. We
also show evidence that this strategy may be broadly applicable to improving
GAN training in other data modalities.
