Survey of transfer learning

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Survey of transfer learning

A survey of transfer learning – Springer

This survey paper aims to provide a researcher interested in transfer learning with an overview of related works, examples of applications that are addressed by transfer learn- ing, and issues and solutions that are relevant to the field of transfer learning.

The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. Background The field of data mining and machine learning has been widely and successfully used in many applications where patterns from past information (training data) can be extracted in order to predict future outcomes [ 129 ].

A Survey on Transfer Learning – Hong Kong University of Science …

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We then briefly survey the history of transfer learning, give a unified definition of transfer learning and categorize transfer learning into three different settings (given in Table 2 and Figure 2). For each setting, we review different approaches, given in Table 3 in detail. After that, in Section 6, we review some current research on the topic of “negative transfer”, which happens …

This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift.

FAQ survey of transfer learning

Is there a survey paper on transfer learning?

This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work.

How many transfer learning approaches are there?

Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, espe- cially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the

What is an example of transfer learning?

Transfer learning, in contrast, allows the domains, tasks, and distributions used in training and testing to be different. In the real world, we observe many examples of transfer learning. For example, we may find that learning to recognize apples might help to recognize pears.

What are the advantages of transfer learning solutions?

Correspondence to Karl Weiss. The majority of transfer learning solutions surveyed are complex and implemented with non-trivial software. It is a great advantage for a researcher to have access to software implementations of transfer learning solutions so comparisons with competing solutions are facilitated more quickly and fairly.

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