Learning to Generate Chairs with Generative Adversarial Nets

Abstract: Generative adversarial networks (GANs) has gained tremendous popularity lately due to an ability to reinforce quality of its predictive model with generated objects and the quality of the generative model with and supervised feedback. GANs allow to synthesize images with a high degree of realism. However, the learning process of such models is a very complicated optimization problem and certain limitation for such models were found. It affects the choice of certain layers and nonlinearities when designing architectures. In particular, it does not allow to train convolutional GAN models with fully-connected hidden layers. In our work, we propose a modification of the previously described set of rules, as well as new approaches to designing architectures that will allow us to train more powerful GAN models. We show the effectiveness of our methods on the problem of synthesizing projections of 3D objects with the possibility of interpolation by class and view point.