  Learning to transfer visual attributes requires supervision dataset.
Corresponding images with varying attribute values with the same identity are
required for learning the transfer function. This largely limits their
applications, because capturing them is often a difficult task. To address the
issue, we propose an unsupervised method to learn to transfer visual attribute.
The proposed method can learn the transfer function without any corresponding
images. Inspecting visualization results from various unsupervised attribute
transfer tasks, we verify the effectiveness of the proposed method.
