  Face images are subject to many different factors of variation, especially in
unconstrained in-the-wild scenarios. For most tasks involving such images, e.g.
expression recognition from video streams, having enough labeled data is
prohibitively expensive. One common strategy to tackle such a problem is to
learn disentangled representations for the different factors of variation of
the observed data using adversarial learning. In this paper, we use a
formulation of the adversarial loss to learn disentangled representations for
face images. The used model facilitates learning on single-task datasets and
improves the state-of-the-art in expression recognition with an accuracy
of60.53%on the AffectNetdataset, without using any additional data.
