Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter

This study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. The proposed approach addresses the challenges posed by domain shift by effectively managing multiple interconnected tasks through the principles of adversarial training. Utilizing the widely recognized Office31 dataset, we assess the efficacy of our model across different domains. Our approach employs a multitask learning paradigm, focusing on adapting to the target domain while leveraging shared feature representations through classification tasks. This strategy ensures that primary and auxiliary tasks incorporate domain-invariant properties, allowing for robust adaptation to varying domains. The results reveal significant improvements in adaptation performance when compared to conventional domain adaptation techniques. For instance, the accuracy of our AML model in adapting from the webcam domain to the dslr domain reached 88.54%, surpassing 86.46% for models without adversarial training and 78.91% for those lacking categorical training. Furthermore, we conduct a comprehensive examination of how different hyperparameters influence model performance, enhancing our understanding of the fundamental mechanisms underlying Adversarial Multitask Learning for domain adaptability. Overall, this paper contributes significantly to the field of domain adaptation by introducing the AML framework, underscoring the importance of further exploration of multitask learning paradigms and adversarial training to improve domain adaptation in real-world scenarios.