Abstract

Recent domain adaptation methods successfully learn cross- domain transforms to map points between source and target domains. Yet, these methods are either restricted to a single training domain, or assume that the separation into source domains is known a priori. How- ever, most available training data contains multiple unknown domains. In this paper, we present both a novel domain transform mixture model which outperforms a single transform model when multiple domains are present, and a novel constrained clustering method that successfully dis- covers latent domains. Our discovery method is based on a novel hier- archical clustering technique that uses available ob ject category infor- mation to constrain the set of feasible domain separations. To illustrate the effectiveness of our approach we present experiments on two com- monly available image datasets with and without known domain labels: in both cases our method outperforms baseline techniques which use no domain adaptation or domain adaptation methods that presume a single underlying domain shift.