Since annotating and curating large datasets is very expensive, there is a need to transfer the knowledge from existing annotated datasets to unlabelled data. Data that is relevant for a specific application, however, usually differs from publicly available datasets since it is sampled from a different domain. While domain adaptation methods compensate for such a domain shift, they assume that all categories in the target domain are known and match the categories in the source domain. Since this assumption is violated under real-world conditions, we propose an approach for open set domain adaptation where the target domain contains instances of categories that are not present in the source domain. The proposed approach achieves state-of-the-art results on various datasets for image classification and action recognition. Since the approach can be used for open set and closed set domain adaptation, as well as unsupervised and semi-supervised domain adaptation, it is a versatile tool for many applications.

中文翻译：

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用于图像和动作识别的开集域适应


由于注释和整理大型数据集非常昂贵，因此需要将知识从现有注释数据集转移到未标记数据。然而，与特定应用程序相关的数据通常与公开可用的数据集不同，因为它是从不同的域采样的。虽然域适应方法补偿了这种域转移，但它们假设目标域中的所有类别都是已知的并且与源域中的类别匹配。由于在现实条件下违反了这一假设，因此我们提出了一种开放集域适应方法，其中目标域包含源域中不存在的类别实例。所提出的方法在图像分类和动作识别的各种数据集上实现了最先进的结果。由于该方法可用于开集和闭集域适应，以及无监督和半监督域适应，因此它是适用于许多应用的多功能工具。