A Survey on Deep Domain Adaptation

Abstract

In the practical implementation of deep learning, challenges such as a heavy reliance on large datasets, high costs associated with data annotation, and significant computational resource consumption can lead to issues like incomplete datasets and inaccurate annotations. These factors negatively impact the model's generalization capabilities and overall performance. To address these concerns, recent research has increasingly focused on adaptive technologies. Domain adaptation seeks to transfer abundant labeled information from a source domain to an unlabeled target domain, thereby tackling the decline in machine learning model performance when faced with varying data distributions. By fine-tuning and optimizing models, it is possible to bridge gaps between different domains or scenarios effectively; this allows for leveraging knowledge from the source domain to better align with the characteristics of the target domain. Consequently, this approach reduces training and annotation costs while enhancing both accuracy and robustness in real-world applications. This review intends to thoroughly investigate the distinct contributions of two specific techniques: Joint Adversarial Domain Adaptation, and Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation. Through this examination, we aim to uncover strategies for more effectively addressing challenges posed by limited data availability, high expenses, and substantial computing resource demands in deep learning by generating similar high-quality data that further minimizes discrepancies between source and target domains ultimately leading to marked improvements in model adaptability and generalization abilities while facilitating efficient deployment and performance enhancement of deep learning technologies across various practical application scenarios.