在受限空间中逃离塌陷模式
Escaping from Collapsing Modes in a Constrained Space
摘要：
生成对抗网络（GANs）在训练过程中经常遭受不可预测的模式崩溃。研究了边界平衡生成对抗网络（BEGAN）的模式坍塌问题。尽管BEGAN具有生成高质量图像的潜力，但我们发现，经过一段时间的训练后，BEGAN往往会在某些模式下崩溃。我们提出了一个新的模型，称为\emph{BEGAN with a Constrained Space}（BEGAN-CS），其中包括潜在的空间约束的损失函数。我们表明，BEGAN-CS可以显着提高训练的稳定性和抑制模式坍塌，既不增加模型的复杂性或降低图像质量。此外，我们可视化的潜在向量的分布，阐明潜在空间约束的效果。实验结果表明，我们的方法具有额外的优势，能够在小数据集上进行训练，并生成与给定的真实图像相似的图像，但具有指定属性的变化。
英文摘要：
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.
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