Unsupervised domain adaptation has becoming a popular research area and many methods with promising results have been proposed in past few years. It is normally assumed that labelled (training) data in source domain is available. However, owing to the data privacy or commercial data confidentiality, company may not be allowed (or not willing) to distribute/share personal data to other parties without individual’s consent. For example, a new General Data Protection Regulation (GDPR) has been approved in April 2016 in Europe (to replace the existing data protection directive) and to be implemented in May 2018. GDPR has strengthen the restriction in transferring personal data from one electronic system to another. Moreover, under the GDPR, individual has the right to erasure his/her personal data from the system. As such, training data in source domain will not be available. Under this scenario, source data distribution is hard, if not impossible to estimate. With only a source classifier and target unlabled data, this project will propose to extract source and target regions from label neighbours which could (i) induce source and target data distributions, and (ii) facilitate cross domain alignment.
This project is supported by the Research Grants Council (RGC), Hong Kong SAR, China (Project HKBU12200518)
For further information on this research topic, please contact Prof. P.C. YUEN.