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The standard closed-set domain adaptation approaches seek to mitigatedistribution discrepancies between two domains under the constraint of bothsharing identical label sets. However, in realistic scenarios, finding anoptimal source domain with identical label space is a challenging task. Partialdomain adaptation alleviates this problem of procuring a labeled dataset withidentical label space assumptions and addresses a more practical scenario wherethe source label set subsumes the target label set. This, however, presents afew additional obstacles during adaptation. Samples with categories private tothe source domain thwart relevant knowledge transfer and degrade modelperformance. In this work, we try to address these issues by couplingvariational information and adversarial learning with a pseudo-labelingtechnique to enforce class distribution alignment and minimize the transfer ofsuperfluous information from the source samples. The experimental findings innumerous cross-domain classification tasks demonstrate that the proposedtechnique delivers superior and comparable accuracy to existing methods.

Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation