Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning

Abstract

Most supervised machine learning techniques, such as classification, rely on some underlying assumptions, such as: (a) the data distributions during training and prediction time are similar; (b) the label space during training and prediction time are similar; and (c) the feature space between the training and prediction time remains the same. In many real-world scenarios, these assumptions do not hold due to the changing nature of the data.