  Deep networks have been successfully applied to learn transferable features
for adapting models from a source domain to a different target domain. In this
paper, we present joint adaptation networks (JAN), which learn a transfer
network by aligning the joint distributions of multiple domain-specific layers
across domains based on a joint maximum mean discrepancy (JMMD) criterion.
Adversarial training strategy is adopted to maximize JMMD such that the
distributions of the source and target domains are made more distinguishable.
Learning can be performed by stochastic gradient descent with the gradients
computed by back-propagation in linear-time. Experiments testify that our model
yields state of the art results on standard datasets.
