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.

通过对Boundary Equilibrium Generative Adversarial Network（BEGAN）的研究，提出了一种新的模型BEGAN with a Constrained Space（BEGAN-CS），在损失函数中包含了潜在空间约束，显著提高了训练稳定性并抑制模式崩溃，而不会增加模型复杂度或降低图像质量。

在受限空间中逃脱崩溃模式