  Given an existing system learned from previous source domains, it is
desirable to adapt the system to new domains without accessing and forgetting
all the previous domains in some applications. This problem is known as domain
expansion. Unlike traditional domain adaptation in which the target domain is
the domain defined by new data, in domain expansion the target domain is formed
jointly by the source domains and the new domain (hence, domain expansion) and
the label function to be learned must work for the expanded domain.
Specifically, this paper presents a method for unsupervised multi-source domain
expansion (UMSDE) where only the pre-learned models of the source domains and
unlabelled new domain data are available. We propose to use the predicted class
probability of the unlabelled data in the new domain produced by different
source models to jointly mitigate the biases among domains, exploit the
discriminative information in the new domain, and preserve the performance in
the source domains. Experimental results on the VLCS, ImageCLEF_DA and PACS
datasets have verified the effectiveness of the proposed method.
