Domain Adaptation: Challenges, Methods, Datasets, and Applications

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well another set of data (target domain), which is different but has similar properties as the source domain. Domain Adaptation (DA) strives to alleviate this problem and great potential in its application practical settings, real-world scenarios, industrial applications many domains. Various DA methods aimed at individual domains have been reported last few years; however, there no comprehensive survey that encompasses all these domains, focuses datasets available, relevant each domain, importantly challenges. To end, paper discusses how can help DNNs work efficiently settings by reviewing techniques. We considered five domains: computer vision, natural language processing, speech, time-series, multi-modal data. present a taxonomy, including methods, datasets, challenges, corresponding Our goal discuss use cases implementation for those. final aim provide future research directions based evolving results, used, applications.