Partial Transfer Learning with Selective Adversarial Networks

Understand

Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains.

• Existing domain adversarial networks assume fully shared label space across domains.
• In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains.
• This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space.