Multi-adversarial domain adaptation method based on feature correction
Domain adaptation can transfer labeled source domain information to an unlabeled but related target do-main 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 pro-posed.An attention mechanism to highlight transferable regions was introduced in this method and a feature correc-tion 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 learn-ing demonstrate the effectiveness of the proposed method.
万方数据
维普
