  Recent studies have shown how disentangling images into content and feature
spaces can provide controllable image translation/ manipulation. In this paper,
we propose a framework to enable utilizing discrete multi-labels to control
which features to be disentangled, i.e., disentangling label-specific
fine-grained features for image manipulation (dubbed DLGAN). By mapping the
discrete label-specific attribute features into a continuous prior
distribution, we leverage the advantages of both discrete labels and reference
images to achieve image manipulation in a hybrid fashion. For example, given a
face image dataset (e.g., CelebA) with multiple discrete fine-grained labels,
we can learn to smoothly interpolate a face image between black hair and blond
hair through reference images while immediately controlling the gender and age
through discrete input labels. To the best of our knowledge, this is the first
work that realizes such a hybrid manipulation within a single model. More
importantly, it is the first work to achieve image interpolation between two
different domains without requiring continuous labels as the supervision.
Qualitative and quantitative experiments demonstrate the effectiveness of the
proposed method.
