![]() To further make aging effects of the synthesized face images more clear, a pre-trained deep face recognition model and a pre-trained deep age estimation model are introduced to preserve personal identity and age similarity respectively. In order to improve the capabilities of the generator and discriminator, we add an auxiliary multi-class classifier on top of the original discriminator and train the discriminator in a multi-task learning setting. We formulate facial age synthesis as an unsupervised multi-domain image-to-image translation problem, and devise a novel generative framework using only a single generative adversarial network, dubbed FaceGAN which synthesizes photo-realistic face images with aging effects with unpaired samples and achieves face age progression and regression in a holistic framework. ![]()
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