  Generative adversarial networks (GANs) are one of the most widely used
generative models. GANs can learn complex multi-modal distributions, and
generate real-like samples. Despite the major success of GANs in generating
synthetic data, they might suffer from unstable training process, and mode
collapse. In this paper, we introduce a new GAN architecture called variance
enforcing GAN (VARGAN), which incorporates a third network to introduce
diversity in the generated samples. The third network measures the diversity of
the generated samples, which is used to penalize the generator's loss for low
diversity samples. The network is trained on the available training data and
undesired distributions with limited modality. On a set of synthetic and
real-world image data, VARGAN generates a more diverse set of samples compared
to the recent state-of-the-art models. High diversity and low computational
complexity, as well as fast convergence, make VARGAN a promising model to
alleviate mode collapse.
