  Generative adversarial networks (GANs) often suffer from unpredictable
mode-collapsing during training. We study the issue of mode collapse of
Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of
the state-of-the-art generative models. Despite its potential of generating
high-quality images, we find that BEGAN tends to collapse at some modes after a
period of training. We propose a new model, called \emph{BEGAN with a
Constrained Space} (BEGAN-CS), which includes a latent-space constraint in the
loss function. We show that BEGAN-CS can significantly improve training
stability and suppress mode collapse without either increasing the model
complexity or degrading the image quality. Further, we visualize the
distribution of latent vectors to elucidate the effect of latent-space
constraint. The experimental results show that our method has additional
advantages of being able to train on small datasets and to generate images
similar to a given real image yet with variations of designated attributes
on-the-fly.
