  Unsupervised domain adaptation (UDA) aims to train a target classifier with
labeled samples from the source domain and unlabeled samples from the target
domain. Classical UDA learning bounds show that target risk is upper bounded by
three terms: source risk, distribution discrepancy, and combined risk. Based on
the assumption that the combined risk is a small fixed value, methods based on
this bound train a target classifier by only minimizing estimators of the
source risk and the distribution discrepancy. However, the combined risk may
increase when minimizing both estimators, which makes the target risk
uncontrollable. Hence the target classifier cannot achieve ideal performance if
we fail to control the combined risk. To control the combined risk, the key
challenge takes root in the unavailability of the labeled samples in the target
domain. To address this key challenge, we propose a method named E-MixNet.
E-MixNet employs enhanced mixup, a generic vicinal distribution, on the labeled
source samples and pseudo-labeled target samples to calculate a proxy of the
combined risk. Experiments show that the proxy can effectively curb the
increase of the combined risk when minimizing the source risk and distribution
discrepancy. Furthermore, we show that if the proxy of the combined risk is
added into loss functions of four representative UDA methods, their performance
is also improved.
