A Survey of Transfer Learning and Categories

In a variety of real-world scenarios, techniques such as machine learning and data mining are applied. Traditional machine learning frameworks suppose that training data and testing data come from the same domain, have the same feature space, and have the same feature space distribution. This assumption, however, is capable of being applied in certain realistic machine learning cases, especially when gathering training data is prohibitively costly or impossible. As a result, high-performance learners must be developed using data that is more conveniently gathered from various domains. Transfer learning is the name given to this method; it is a learning environment based on a person's capacity to extrapolate information through activities to learn more quickly. Transfer learning tries to establish a structure for applying previous knowledge learned skills to tackle new but related issues more swiftly and efficiently. Transfer learning methodologies, in opposition to traditional machine learning technics, use data from auxiliary domains to enhance predictive modelling of distinct data patterns in the present domain. Transfer learning focuses on improving target participants' performance on target domains by passing data or knowledge from numerous but similar source domains. As a result, the reliance on a various number of target-domain available data for building target learners can be minimized. This survey paper explains transfer learning categories based on problems and solutions and explains experiment results and examples of its application and perspective related to transfer learning. Also, it provides a concise overview of the processes and methods of transfer learning, which may aid readers in better understanding the current research state and idea.