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Targeted Adversarial Discriminative Domain Adaptation

机译：有针对性的对抗歧途歧视域适应

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摘要

Domain adaptation is a technology enabling Aided Target Recognition (AiTR) and other algorithms for environments and targets where data or labeled data is scarce. Recent advances in unsupervised domain adaptation have demonstrated excellent performance but only when the domain shift is relatively small. This paper proposes Targeted Adversarial Discriminative Domain Adaptation (T-ADDA), a semi-supervised domain adaptation method by extending the Adversarial Discriminative Domain Adaptation (ADDA) framework. By providing at least one labeled target image per class, T-ADDA significantly boosts the performance of ADDA and is applicable to the challenging scenario where the set of targets in the source and target domains are not the same. The efficacy of T-ADDA is demonstrated by several experiments using the Modified National Institute of Standards and Technology (MNIST), Street View House Numbers (SVHN), and Devanagari Handwritten Character (DHC) datasets and then extended to aerial image datasets Aerial Image Data (AID) and University of California, Merced (UCM).
机译：域适应是一种技术支持目标识别（AITR）和其他算法，包括数据或标记数据稀缺的环境和目标算法。无监督域适应的最近进步已经表现出出色的性能，但仅当域移相对较小时。本文提出了靶向对抗鉴别域适应（T-ADDA），通过扩展对抗鉴别域适应（ADDA）框架来进行半监督域适应方法。通过提供每个类的至少一个标记的目标图像，T-Adda显着提高了ADDA的性能，并且适用于源极和靶域中的目标集合的具有挑战性的场景不相同。使用修改的国家标准和技术研究所（MNIST），街道视图房屋号（SVHN）和Devanagari手写字符（DHC）数据集，并扩展到空中图像数据集空中图像数据（艾滋病）和加州大学，默塞德（UCM）。

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