Abstract:
Unsupervised domain adaptation (UDA) deals with the adaptation process of a given source domain with labeled training data to a target domain for which only unannotated data is available. This is a challenging task as the domain shift leads to degraded performance on the target domain data if not addressed. In this paper, we analyze commonly used UDA classification datasets and discover systematic problems with regard to dataset setup, ground truth ambiguity and annotation quality. We manually clean the most popular UDA dataset in the research area (Office-31) and quantify the negative effects of inaccurate annotations through thorough experiments. Based on these insights, we collect the Adaptiope dataset - a large scale, diverse UDA dataset with synthetic, product and real world data - and show that its transfer tasks provide a challenge even when considering recent UDA algorithms. Our datasets are available at https://gitlab.com/tringwald/adaptiope.
