  Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a
task learned in a source domain to a target domain, while data from target
domain are not available. In this work, we address learning feature
representations which are invariant to and shared among different domains
considering task characteristics for ZDA. To this end, we propose a method for
task-guided ZDA (TG-ZDA) which employs multi-branch deep neural networks to
learn feature representations exploiting their domain invariance and
shareability properties. The proposed TG-ZDA models can be trained end-to-end
without requiring synthetic tasks and data generated from estimated
representations of target domains. The proposed TG-ZDA has been examined using
benchmark ZDA tasks on image classification datasets. Experimental results show
that our proposed TG-ZDA outperforms state-of-the-art ZDA methods for different
domains and tasks.
