Domain Adaptation in Machine Learning: A Practical Simulation Study

Domain adaptation (DA) is a critical technique in machine learning, designed to alleviate distribution differences between training and test sets by leveraging information from similar datasets. This paper presents a practical simulation of both widely-used and recent DA techniques, with a specific focus on unsupervised learning scenarios where labels are only available in the source domain. We thoroughly investigate the impact of these methods on various publicly available datasets, providing detailed explanations of our findings. Furthermore, we conduct an ablation study and elaborate extensively on the simulation results. Our study offers valuable information on the resilience of selected DA algorithms, highlighting the importance of appropriate datasets and neural network architectures with training methodologies to achieve optimal performance in DA tasks.