  Knowledge transfer between heterogeneous source and target networks and tasks
has received a lot of attention in recent times as large amounts of quality
labeled data can be difficult to obtain in many applications. Existing
approaches typically constrain the target deep neural network (DNN) feature
representations to be close to the source DNNs feature representations, which
can be limiting. We, in this paper, propose a novel adversarial multi-armed
bandit approach that automatically learns to route source representations to
appropriate target representations following which they are combined in
meaningful ways to produce accurate target models. We see upwards of 5\%
accuracy improvements compared with the state-of-the-art knowledge transfer
methods on four benchmark (target) image datasets CUB200, Stanford Dogs, MIT67,
and Stanford40 where the source dataset is ImageNet. We qualitatively analyze
the goodness of our transfer scheme by showing individual examples of the
important features focused on by our target network at different layers
compared with the (closest) competitors. We also observe that our improvement
over other methods is higher for smaller target datasets making it an effective
tool for small data applications that may benefit from transfer learning.
