Domain adaptation is a technology enabling aided target recognition and other algorithms for environments and targets with data or labeled data that is scarce. Recent advances in unsupervised domain adaptation have demonstrated excellent performance but only when the domain shift is relatively small. We proposed targeted adversarial discriminative domain adaptation (T-ADDA), a semi-supervised domain adaptation method that extends the ADDA framework. By providing at least one labeled target image per class, used as a cue to guide the adaption, T-ADDA significantly boosts the performance of ADDA and is applicable to the challenging scenario in which the sets of targets in the source and target domains are not the same. The efficacy of T-ADDA is demonstrated by cross-domain, cross-sensor, and cross-target experiments using the common digits datasets and several aerial image datasets. Results demonstrate an average increase of 15% improvement with T-ADDA over ADDA using just a few labeled images when adapting to a small domain shift and afforded a 60% improvement when adapting to large domain shifts.

中文翻译：

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有针对性的对抗判别域适应

域自适应是一种技术，可以为具有稀缺数据或标记数据的环境和目标提供辅助目标识别和其他算法。无监督域适应的最新进展已经证明了出色的性能，但仅当域偏移相对较小时才如此。我们提出了有针对性的对抗判别域适应 (T-ADDA)，这是一种扩展 ADDA 框架的半监督域适应方法。通过为每个类别提供至少一个标记的目标图像，用作指导适应的提示，T-ADDA 显着提高了 ADDA 的性能，适用于具有挑战性的场景，其中源域和目标域中的目标集不是相同。T-ADDA 的功效通过跨域、跨传感器、以及使用公共数字数据集和多个航拍图像数据集的跨目标实验。结果表明，在适应小域偏移时，使用 T-ADDA 比仅使用少量标记图像的 ADDA 平均提高 15%，在适应大域偏移时提高 60%。