Extending Partial Domain Adaptation Algorithms to the Open-Set Setting

Abstract

Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA.

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization

“…The goal of domain adaptation algorithms is to create ML models that will demonstrate robust performance when applied to a different domain [ 35 ]. By “domain” we refer to the feature space that describes the problem at hand [ 36 ].…”