Virtual to Real-World Transfer Learning: A Systematic Review
Mahesh Ranaweera, Qusay H. Mahmoud
Abstract
Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and real-world.