Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
This addresses a specific bottleneck in generative models for high-resolution image synthesis, offering incremental improvements in training stability and quality for applications like face editing.
The paper tackles the problem of training instability and poor sample quality in high-resolution image-to-image translation by proposing a biphasic learning framework for GANs, achieving results at 1024^2 resolution and significantly outperforming existing methods in face-related synthesis tasks.
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.