How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
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 …