  Large-scale labeled training datasets have enabled deep neural networks to
excel on a wide range of benchmark vision tasks. However, in many applications
it is prohibitively expensive or time-consuming to obtain large quantities of
labeled data. To cope with limited labeled training data, many have attempted
to directly apply models trained on a large-scale labeled source domain to
another sparsely labeled target domain. Unfortunately, direct transfer across
domains often performs poorly due to domain shift and dataset bias. Domain
adaptation is the machine learning paradigm that aims to learn a model from a
source domain that can perform well on a different (but related) target domain.
In this paper, we summarize and compare the latest unsupervised domain
adaptation methods in computer vision applications. We classify the non-deep
approaches into sample re-weighting and intermediate subspace transformation
categories, while the deep strategy includes discrepancy-based methods,
adversarial generative models, adversarial discriminative models and
reconstruction-based methods. We also discuss some potential directions.
