  Despite that the performance of image-to-image translation has been
significantly improved by recent progress in generative models, current methods
still suffer from severe degradation in training stability and sample quality
when applied to the high-resolution situation. In this work, we present a novel
training framework for GANs, namely biphasic learning, to achieve
image-to-image translation in multiple visual domains at $1024^2$ resolution.
Our core idea is to design an adjustable objective function that varies across
training phases. Within the biphasic learning framework, we propose a novel
inherited adversarial loss to achieve the enhancement of model capacity and
stabilize the training phase transition. Furthermore, we introduce a
perceptual-level consistency loss through mutual information estimation and
maximization. To verify the superiority of the proposed method, we apply it to
a wide range of face-related synthesis tasks and conduct experiments on
multiple large-scale datasets. Through comprehensive quantitative analyses, we
demonstrate that our method significantly outperforms existing methods.
