Abstract:Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baseline algorithms are not tested to the extent that they should be. Furthermore, little attention has been paid to validation methods, i.e. the methods for estimating the accuracy of a model in the absence of target domain labels. This is despite the fact that validation methods are a crucial component of any UDA train/val pipeline. In th… Show more
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