  Recently there has been an enormous interest in generative models for images
in deep learning. In pursuit of this, Generative Adversarial Networks (GAN) and
Variational Auto-Encoder (VAE) have surfaced as two most prominent and popular
models. While VAEs tend to produce excellent reconstructions but blurry
samples, GANs generate sharp but slightly distorted images. In this paper we
propose a new model called Variational InfoGAN (ViGAN). Our aim is two fold:
(i) To generated new images conditioned on visual descriptions, and (ii) modify
the image, by fixing the latent representation of image and varying the visual
description. We evaluate our model on Labeled Faces in the Wild (LFW), celebA
and a modified version of MNIST datasets and demonstrate the ability of our
model to generate new images as well as to modify a given image by changing
attributes.
