Learning to generate high resolution images with bilateral adversarial networks

Learning the generative models of multimedia data such as audio, images and video is a challenging image analysis problem because of the infinitely many manifestations of just one concept, and the potentially large number of concepts that can be encountered. Deep learning methods have proven useful for handling such complex, high dimensional datasets by taking advantage of the use of shared and distributed representation while learning. In this work, we build on the recent successes of several deep learning techniques and propose the "bilateral adversarial network". We demonstrate its efficacy by performing quantitative tests on the standard benchmark datasets, and qualitative tests on large, diverse complex datasets (on over two million high-resolution images).