Multi-adversarial domain adaptation method based on feature correction

Domain adaptation can transfer labeled source domain information to an unlabeled but related target domain by aligning the distribution of source domain and target domain. However, most existing methods only align the low-level feature distributions of the source and target domains, failing to capture fine-grained information within the samples. To address this limitation, a feature correction-based multi-adversarial domain adaptation method was proposed. An attention mechanism to highlight transferable regions was introduced in this method and a feature correction module was deployed to align the high-level feature distributions between the two domains, further reducing domain discrepancies. Additionally, to prevent individual classifiers from overfitting their own noisy pseudo-labels,dual classifier co-training was proposed and the feature aggregation property of graph neural networks was utilized to generate more accurate source domain labels. Extensive experiments on three benchmark datasets for transfer