The capability of “transferring” learned classifiers from one domain, or set of targets, to classify different targets or the same targets but in different domains is of great interest to the United States Air Force. It is an enabling technology for the USAF to build Aided Target Recognition and other algorithms for environments and targets where the data or labeled data is scarce. In this project, Intelligent Fusion Technologies, Inc. and Rochester Institute of Technology propose a deep transfer learning approach to meet the USAF’s need. The proposed work is built upon the recent discriminative adversarial domain adaptation framework with the addition of one labeled image per target class.