  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.
