  In many practical applications, it is often difficult and expensive to obtain
enough large-scale labeled data to train deep neural networks to their full
capability. Therefore, transferring the learned knowledge from a separate,
labeled source domain to an unlabeled or sparsely labeled target domain becomes
an appealing alternative. However, direct transfer often results in significant
performance decay due to domain shift. Domain adaptation (DA) addresses this
problem by minimizing the impact of domain shift between the source and target
domains. Multi-source domain adaptation (MDA) is a powerful extension in which
the labeled data may be collected from multiple sources with different
distributions. Due to the success of DA methods and the prevalence of
multi-source data, MDA has attracted increasing attention in both academia and
industry. In this survey, we define various MDA strategies and summarize
available datasets for evaluation. We also compare modern MDA methods in the
deep learning era, including latent space transformation and intermediate
domain generation. Finally, we discuss future research directions for MDA.
