Adaptive gradient-norm weighting for improved domain adversarial training
Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain by jointly optimizing source classification and domain-alignment objectives. A key practical challenge is selecting the trade-off coefficient λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackag...