  We propose a novel generative model architecture designed to learn
representations for images that factor out a single attribute from the rest of
the representation. A single object may have many attributes which when altered
do not change the identity of the object itself. Consider the human face; the
identity of a particular person is independent of whether or not they happen to
be wearing glasses. The attribute of wearing glasses can be changed without
changing the identity of the person. However, the ability to manipulate and
alter image attributes without altering the object identity is not a trivial
task. Here, we are interested in learning a representation of the image that
separates the identity of an object (such as a human face) from an attribute
(such as 'wearing glasses'). We demonstrate the success of our factorization
approach by using the learned representation to synthesize the same face with
and without a chosen attribute. We refer to this specific synthesis process as
image attribute manipulation. We further demonstrate that our model achieves
competitive scores, with state of the art, on a facial attribute classification
task.
