We are aiming for an unsupervised visual feature learning algorithm driven by context-based pixel prediction with Deep Convoluted Generative Adversarial Networks(DCGAN) -- which learns to generate missing parts of an input image conditioned on both contextual and perceptual loss. In order to succeed at this task our model need to both understand the content of the entire image , as well as produce plausible prediction for missing/corrupted part.
All Theoretical and Mathematical references from - https://arxiv.org/abs/1607.07539