26-29 October 2015
Machine learning applications rely in general on a large amount of hand labelled examples. However labelling is expensive and time consuming due to the significant amount of human efforts involved. Domain adaptation addresses the problem of leveraging labelled data in one or more related domains, often referred as source domains, when learning a classifier for unseen data in a target domain. Adaptation across domains is a challenging task for many real applications including NLP tasks, spam filtering, speech recognition and various visual applications. In this talk after a brief overview of different types of domain adaptation methods, I will focus mainly on a several visual scenarios and give a more detailed view of a few recent methods.