Iterative Classified Mean Discrepancy In Transfer Learning

Transfer learning is a branch of machine learning. Since labeled training samples are always scarce in target domain and it is expensive and time-consuming to label those samples, however, there are plenty of labeled samples which have some similarity with but no the same as samples of target domain, it is crucial to make advantage of the same knowledge between the two domains. So transferring those useful knowledge from source domain to target domain is the most significant in transfer learning. A lot of previous related work designed methods to close the similarity of the two domain to improve the performance of transfer learning, which is always considered to be the problem of domain adaptation. And the most popular method to solve the problem of domain adaptation is Maximum Mean Discrepancy.In this paper, we introduce Classified Mean Discrepancy which is more efficient than the traditional Maximum Mean Discrepancy. First, we should build a set of pseudo labels for target domain and then calculate the discrepancy of each class between source domain and target domain and add them together to get classified mean discrepancy. By this way, we could simultaneously optimize the structural risk functional and the classified mean discrepancy to finish the method of Classified Mean Discrepancy. More importantly, it is a method which could improve its performance by iterating itself. Via iteration, it could reduce the classified mean discrepancy and rise the accuracy. By comparing with some popular methods in domain adaptation, comprehensive experiments verify the performance of the methods of Classified Mean Discrepancy and Iterative Classified Mean Discrepancy on some popular datasets in transfer learning.