A data-centric review of deep transfer learning with applications to text data

Samar Bashath, Nadeesha Perera, Shailesh Tripathi, Kalifa Manjang, Matthias Dehmer, Frank Emmert Streib

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


In recent years, many applications are using various forms of deep learning models. Such methods are usually based on traditional learning paradigms requiring the consistency of properties among the feature spaces of the training and test data and also the availability of large amounts of training data, e.g., for performing supervised learning tasks. However, many real-world data do not adhere to such assumptions. In such situations transfer learning can provide feasible solutions, e.g., by simultaneously learning from data-rich source data and data-sparse target data to transfer information for learning a target task. In this paper, we survey deep transfer learning models with a focus on applications to text data. First, we review the terminology used in the literature and introduce a new nomenclature allowing the unequivocal description of a transfer learning model. Second, we introduce a visual taxonomy of deep learning approaches that provides a systematic structure to the many diverse models introduced until now. Furthermore, we provide comprehensive information about text data that have been used for studying such models because only by the application of methods to data, performance measures can be estimated and models assessed.

Original languageEnglish
Pages (from-to)498-528
Number of pages31
JournalInformation Sciences
Publication statusPublished - Mar 2022


  • Deep learning
  • Domain adaptation
  • Machine learning
  • Natural language processing
  • Transfer learning


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