RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
This work addresses inefficiencies in GAN-based methods for facial age synthesis, offering a more streamlined approach for applications in entertainment or security, though it is incremental in nature.
The paper tackled the problem of age progression and regression in face images by proposing RoutingGAN, a method that learns various age effects in a single model with partially shared convolution filters, achieving superior performance on two benchmark datasets.
Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditional GANs (cGANs)-based methods can learn various effects between any two age groups in a single model, but are insufficient to characterize some specific patterns due to completely shared convolutions filters; and 2) GANs-based methods can, by utilizing several models to learn effects independently, learn some specific patterns, however, they are cumbersome and require age label in advance. To address these deficiencies and have the best of both worlds, this paper introduces a dropout-like method based on GAN~(RoutingGAN) to route different effects in a high-level semantic feature space. Specifically, we first disentangle the age-invariant features from the input face, and then gradually add the effects to the features by residual routers that assign the convolution filters to different age groups by dropping out the outputs of others. As a result, the proposed RoutingGAN can simultaneously learn various effects in a single model, with convolution filters being shared in part to learn some specific effects. Experimental results on two benchmarked datasets demonstrate superior performance over existing methods both qualitatively and quantitatively.