Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
This addresses data scarcity in face-related applications, but it is incremental as it builds on existing GAN methods with specific modifications.
The paper tackles the problem of synthesizing non-linear facial variations like expressions and poses with limited training data by proposing Differential Generative Adversarial Networks (D-GAN), which achieves photo-realistic face synthesis and improves face expression classifier performance.
In face-related applications with a public available dataset, synthesizing non-linear facial variations (e.g., facial expression, head-pose, illumination, etc.) through a generative model is helpful in addressing the lack of training data. In reality, however, there is insufficient data to even train the generative model for face synthesis. In this paper, we propose Differential Generative Adversarial Networks (D-GAN) that can perform photo-realistic face synthesis even when training data is small. Two discriminators are devised to ensure the generator to approximate a face manifold, which can express face changes as it wants. Experimental results demonstrate that the proposed method is robust to the amount of training data and synthesized images are useful to improve the performance of a face expression classifier.