Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment

HE problem of training a machine learning model in the presence of different training and test distributions is known as domain adaptation [3], [6]-[9]. The goal of domain adaptation is to build a model that performs well on a target distribution while it is trained on a different but related source distribution. One important example is in the sentiment analysis of product reviews [1], where a model is trained on data of a source product category, e. g. kitchen appliances, and it is tested on data of a related category, e. g. books. A second example is the training of image classifiers on unlabeled real images by means of nearly-synthetic images that are fully labeled but which have a distribution that is different [2], [3]. Another example is the content-based depth range adaptation of unlabeled stereoscopic videos by means of labeled data from movies [4], [5]. It is shown in [10], that a classifier's error on the target domain can be bounded in terms of its error on the source domain and a difference between the source and the target domain distribution [10]. This motivated many approaches to first extract features that overcome the distribution difference and subsequently minimize the source error [8], [11]-[13]. With the recent developments in representation learning, approaches have been developed that embed domain adaptation in the feature learning process.