Face Synthesis With a Focus on Facial Attributes Translation Using Attention Mechanisms

Synthesis of face images by translating facial attributes is an important problem in computer vision and biometrics and has a wide range of applications in forensics, entertainment, etc. Recent advances in deep generative networks have made progress in synthesizing face images with certain target facial attributes. However, visualizing and interpreting generative adversarial networks (GANs) is a relatively unexplored area and generative models are still being employed as black-box tools. This paper takes the first step to visually interpret conditional GANs for facial attribute translation by using a gradient-based attention mechanism. Next, a key innovation is to include new learning objectives for knowledge distillation using attention in generative adversarial training, which result in improved synthesized face results, reduced visual confusions and boosted training for GANs in a positive way. Firstly, visual attentions are calculated to provide interpretations for GANs. Secondly, gradient-based visual attentions are used as knowledge to be distilled in a teacher-student paradigm for face synthesis with focus on facial attributes translation tasks in order to improve the performance of the model. Finally, it is shown how “pseudo”-attentions knowledge distillation can be employed during the training of face synthesis networks when teacher and student networks are trained to generate different facial attributes. The approach is validated on facial attribute translation and human expression synthesis with both qualitative and quantitative results being presented.