  Zero-shot learning (ZSL) algorithms typically work by exploiting attribute
correlations to be able to make predictions in unseen classes. However, these
correlations do not remain intact at test time in most practical settings and
the resulting change in these correlations lead to adverse effects on zero-shot
learning performance. In this paper, we present a new paradigm for ZSL that:
(i) utilizes the class-attribute mapping of unseen classes to estimate the
change in target distribution (target shift), and (ii) propose a novel
technique called grouped Adversarial Learning (gAL) to reduce negative effects
of this shift. Our approach is widely applicable for several existing ZSL
algorithms, including those with implicit attribute predictions. We apply the
proposed technique ($g$AL) on three popular ZSL algorithms: ALE, SJE, and
DEVISE, and show performance improvements on 4 popular ZSL datasets: AwA2, aPY,
CUB and SUN. We obtain SOTA results on SUN and aPY datasets and achieve
comparable results on AwA2.
