Adaptive gradient-norm weighting for improved domain adversarial training
Adaptive Gradient-Norm Weighting (AWD), a lightweight scalarization mechanism that dynamically adjusts gradient norms of the classification and alignment losses in UDA consistently improves average performance over fixed and scheduled baselines, with the strongest gains on more difficult transfer settings.