Domain Adaptation for Visual Understanding

Advances in visual understanding in the last two decades have been aided by exemplary progress in machine learning and deep learning methods. One of the principal issues of modern classifiers is generalization toward unseen testing data which may have a distribution different to that of the training set. Further, classifiers need to be adapted to scenarios where training data is made available online. Domain adaptation based machine learning algorithms cater to these specific scenarios where the classifiers are updated for inclusivity and generalizability. Such methods need to encompass the covariate shift so that the trained model gives appreciable performance on the testing data. In this chapter, we categorize, illustrate, and analyze different domain adaptation based machine learning algorithms for visual understanding.