| Summary: | Recently, the advent of generative adversarial networks (GANs) in synthesizing identity-preserving faces has aroused the considerable interest of many scholars. However, face attribute representation learning, which is explicitly disentangled from identity feature and synthesizes identity-preserving face images with high diversity and quality in other datasets, still remains challenging. To cope with that, this paper proposes multi-process training GAN, or MP-GAN for short, which significantly improves the disentangled representation, diversity, and quality. Unlike other existing single-process models that map noise to a final output resolution image in a single training process, MP-GAN divides training into multiple processes. The main idea is to first generate lower resolution images that contain lower frequency feature information through competition and then extract their disentangled facial features to generate a higher resolution image. Furthermore, an identity-preserving image with real identity feature and disentangled facial feature could be generated at the final output resolution training process. The distinct benefits are not only getting diverse facial feature generation but also achieving disentangled representation from the lower resolution training processes and rendering a photo-realistic image that contains high diversity but preserves identity at the final output resolution training process. The high performance of this method is highlighted by quantitative and qualitative comparisons. We conclude that MP-GAN can generate face images featuring high diversity and quality while efficiently preserving identity, thereby significantly outperforming most modern advanced methods.
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