Bi-Discriminator Domain Adversarial Neural Networks With Class-Level Gradient Alignment
Abstract
Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent genre is the bi-discriminator domain adversarial network, which concurrently considers the decision boundaries of both domains during the domain alignment. While effective, methods within this genre still contend with accuracy agnosticism toward the target domain and overconfident estimation in the source domain. Consequently, these two limitations hinder the effectiveness of both decision boundaries. To address the aforementioned challenges, we propose a novel bi-discriminator domain adversarial neural network, denoted as BACG. Specifically, for accuracy awareness of the target domain, we initially devise an optimizable nearest neighbor algorithm for acquiring pseudo-labels of samples, followed by class-level gradient alignment between two domains. This approach explicitly incorporates the accuracy signal from the target domain. To alleviate overconfident estimation in the source domain, we adopt evidential learning theory and develop a multinomial Dirichlet hierarchical model to infer both the classification probability and uncertainty. This approach not only ensures optimal assumptions for the source domain but also guarantees high-quality domain alignment. Additionally, to lower the time overhead caused by pseudo-label assignment, we introduce a memory bank-based variant, namely, fast-BACG, which effectively accelerates the training process at the expense of a slight reduction in accuracy. Extensive experiments and thorough analysis on four benchmark data sets validate the effectiveness and robustness of our algorithm.
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