T2S: Domain Adaptation Via Model-Independent Inverse Mapping and Model Reuse

Abstract

Domain adaptation, which is able to leverage the abundant supervision from the source domain and limited supervision in the target domain to construct a model for the data in the target domain, has drawn significant attentions. Most of the existing domain adaptation methods elaborate to map the information derived from the source domain to the target domain for model construction in the target domain. However, such a 'Source' (S) to 'Target' (T) mapping usually involves 'tailoring' the information from the source domain to fit the target domain, which may lose valuable information in the source domain for model construction. Moreover, such a mapping is usually tightly coupled with the model construction, which is more complex than a separate model construction or mapping construction. In this paper, we provide an alternative way for domain adaptation, named T2S. Instead of mapping the 'S' to 'T' and constructing a model in 'T', we inversely map 'T' to 'S' and reuse the model that has been well-trained with abundant information in 'S' for prediction. Such an approach enjoys the abundant information in source domain for model construction and the simplicity of learning mapping separately with limited supervision in target domain. Experiments on both synthetic and real-world data sets indicate the effectiveness of our framework.