Deep Visual Unsupervised Domain Adaptation for Classification Tasks: A Survey

Learning methods are challenged when there is not enough labeled data. It gets worse when the existing learning data have different distributions in different domains. To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used. This paper surveys such domain adaptation methods that have been used for classification tasks in computer vision. The survey includes the very recent papers on this topic that have not been included in the previous surveys
and introduces a taxonomy by grouping methods published on unsupervised domain adaptation into five groups of: discrepancy-, adversarial-, reconstruction-, representation-, and attention-based methods.