Our goal is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. We introduce a network architecture that preserves the similarities between domains where they exist and models the differences when necessary.
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representations that are invariant to the changes that occur when going from one (…)