Contrastive Learning and Cycle Consistency-based Transductive Transfer
  Learning for Target Annotation
Shoaib Meraj Sami Md Mahedi Hasan Nasser M. Nasrabadi Raghuveer Rao
Annotating automatic target recognition (ATR) is a highly challenging task,
primarily due to the unavailability of labeled data in the target domain.
Hence, it is essential to construct an optimal target domain classifier by
utilizing the labeled information of the source domain images. The transductive
transfer learning (TTL) method that incorporates a CycleGAN-based unpaired
domain translation network has been previously proposed in the literature for
effective ATR annotation. Although this method demonstrates great potential for
ATR, it severely suffers from lower annotation performance, higher Fr\'echet
Inception Distance (FID) score, and the presence of visual artifacts in the
synthetic images. To address these issues, we propose a hybrid contrastive
learning base unpaired domain translation (H-CUT) network that achieves a
significantly lower FID score. It incorporates both attention and entropy to
emphasize the domain-specific region, a noisy feature mixup module to generate
high variational synthetic negative patches, and a modulated noise contrastive
estimation (MoNCE) loss to reweight all negative patches using optimal
transport for better performance. Our proposed contrastive learning and
cycle-consistency-based TTL (C3TTL) framework consists of two H-CUT networks
and two classifiers. It simultaneously optimizes cycle-consistency, MoNCE, and
identity losses. In C3TTL, two H-CUT networks have been employed through a
bijection mapping to feed the reconstructed source domain images into a
pretrained classifier to guide the optimal target domain classifier. Extensive
experimental analysis conducted on three ATR datasets demonstrates that the
proposed C3TTL method is effective in annotating civilian and military
vehicles, as well as ship targets.