# Learning to Generate Chairs with Generative Adversarial Nets

## TL;DR

This paper introduces new architectural modifications and training methods for GANs, enabling the synthesis of realistic 3D object projections with improved quality and interpolation capabilities.

## Contribution

The authors propose novel design rules and approaches that allow training more powerful convolutional GAN models, overcoming previous limitations.

## Key findings

- Effective synthesis of 3D object projections
- Ability to interpolate by class and viewpoint
- Enhanced training stability and model quality

## 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.