Multi-Source Domain Adaptation with Fuzzy-Rule based Deep Neural Networks

Unsupervised domain adaptation provides a variety of methods to leverage the previously gained knowledge from a labeled source domain to help complete a task from a similar unlabeled target domain. Many existing methods focus on transferring knowledge across single source and single target domains, while few studies deal with multi-source domain adaptation, which is more realistic and challengeable. Existing multi-source domain adaptation methods rarely consider the uncertainty of the transformed knowledge resulting from limited information in target domain. A fuzzy system allows imprecision and ambiguity within transfer, thus it can deal with problems with uncertainty. This work proposes a multi-source domain adaptation method with fuzzy-rule based deep neural networks (MDAFuz). The proposed method first extracts multi-view adapted features and pre-trains source classifiers. Using the learned features and classifiers, training samples are then split into multiple clusters, hence fuzzy rules can be built to learn new classifiers. At the same time, the cluster discriminator is trained to define the membership. Finally, by measuring the similarities among source and target domains using the pseudo target labels and a domain discriminator, the target task is completed by combining all source classifiers with regard to the learned weights. The experiment results on real-world visual datasets show the superiority of the proposed method.