  The standard closed-set domain adaptation approaches seek to mitigate
distribution discrepancies between two domains under the constraint of both
sharing identical label sets. However, in realistic scenarios, finding an
optimal source domain with identical label space is a challenging task. Partial
domain adaptation alleviates this problem of procuring a labeled dataset with
identical label space assumptions and addresses a more practical scenario where
the source label set subsumes the target label set. This, however, presents a
few additional obstacles during adaptation. Samples with categories private to
the source domain thwart relevant knowledge transfer and degrade model
performance. In this work, we try to address these issues by coupling
variational information and adversarial learning with a pseudo-labeling
technique to enforce class distribution alignment and minimize the transfer of
superfluous information from the source samples. The experimental findings in
numerous cross-domain classification tasks demonstrate that the proposed
technique delivers superior and comparable accuracy to existing methods.
