Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence

Often domain adaptation is performed using a discriminator (domain One such adversarial domain-invariant feature learning method classifier) to learn domain-invariant feature representations is the domain-adversarial neural network (DANN) [14, 15], which so that a classifier trained on labeled source data will generalize is a typical baseline for other variants. This method consists of a feature well to unlabeled target data. A line of research stemming from extractor network followed by two additional networks: a task semi-supervised learning uses pseudo labeling to directly generate classifier and a domain classifier (Figure 1). The network is updated "pseudo labels" for the unlabeled target data and trains a classifier by two competing objectives: (1) the feature extractor followed by on the now-labeled target data, where the samples are selected the task classifier learns to correctly classify the labeled source data or weighted based on some measure of confidence. In this paper, while the domain classifier learns to correctly predict whether the we propose multi-purposing the discriminator to not only aid in features originated from source or target data, and (2) the feature producing domain-invariant representations but also to provide extractor learns to make the domain classifier predict the domain pseudo labeling confidence.