AAII Day Presentation 3: Zhen Fang

Open Set Domain Adaptation: Theoretical Bound and Algorithm

TIME: 10:20am - 10:40am

SPEAKER: Zhen Fang

ABSTRACT:
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain--the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set.

In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound.

The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

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Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.