  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.
