  A long standing problem in visual object categorization is the ability of
algorithms to generalize across different testing conditions. The problem has
been formalized as a covariate shift among the probability distributions
generating the training data (source) and the test data (target) and several
domain adaptation methods have been proposed to address this issue. While these
approaches have considered the single source-single target scenario, it is
plausible to have multiple sources and require adaptation to any possible
target domain. This last scenario, named Domain Generalization (DG), is the
focus of our work. Differently from previous DG methods which learn domain
invariant representations from source data, we design a deep network with
multiple domain-specific classifiers, each associated to a source domain. At
test time we estimate the probabilities that a target sample belongs to each
source domain and exploit them to optimally fuse the classifiers predictions.
To further improve the generalization ability of our model, we also introduced
a domain agnostic component supporting the final classifier. Experiments on two
public benchmarks demonstrate the power of our approach.
