  Representation disentanglement aims at learning interpretable features, so
that the output can be recovered or manipulated accordingly. While existing
works like infoGAN and AC-GAN exist, they choose to derive disjoint attribute
code for feature disentanglement, which is not applicable for existing/trained
generative models. In this paper, we propose a decomposition-GAN (dec-GAN),
which is able to achieve the decomposition of an existing latent representation
into content and attribute features. Guided by the classifier pre-trained on
the attributes of interest, our dec-GAN decomposes the attributes of interest
from the latent representation, while data recovery and feature consistency
objectives enforce the learning of our proposed method. Our experiments on
multiple image datasets confirm the effectiveness and robustness of our dec-GAN
over recent representation disentanglement models.
