Generative Adversarial Networks for Heterogeneous Unsupervised Domain Adaptation Detection
Homayoun Safarpour Motealegh Mahalegi, Amirfarhad Farhadi, György Molnár, Enikő Nagy · 2024
Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies regarding sensitivity and specificity, with improvements to 100% and 99%, respectively.