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.

翻译：自动目标识别（ATR）的标注是一项极具挑战性的任务，主要由于目标域中缺乏标注数据。因此，利用源域图像的标注信息构建最优的目标域分类器至关重要。已有文献提出一种结合基于CycleGAN的非配对域翻译网络的直推式迁移学习（TTL）方法，用于实现有效的ATR标注。尽管该方法在ATR中展现出巨大潜力，但其存在标注性能较低、弗雷歇初始距离（FID）分数较高以及合成图像中出现视觉伪影等问题。为解决这些问题，我们提出了一种混合对比学习基础的非配对域翻译（H-CUT）网络，该网络实现了显著更低的FID分数。它整合了注意力机制和熵来强调域特定区域，引入噪声特征混合模块生成高变异性合成负样本块，并采用调制噪声对比估计（MoNCE）损失通过最优传输对所有负样本块进行重新加权以提升性能。我们提出的基于对比学习和循环一致性的直推式迁移学习（C3TTL）框架包含两个H-CUT网络和两个分类器。该框架同时优化了循环一致性、MoNCE和身份损失。在C3TTL中，通过双射映射使用两个H-CUT网络，将重建的源域图像输入预训练分类器，以指导最优目标域分类器。在三个ATR数据集上进行的广泛实验分析表明，所提出的C3TTL方法在标注民用车辆、军用车辆以及舰船目标方面是有效的。