Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes
Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes
复制标题
DOI：
10.1109/wacv45572.2020.9093298
复制
发表时间：
2020-03
期刊：
影响因子：
--
通讯作者：
Masato Ishii;Takashi Takenouchi;Masashi Sugiyama
中科院分区：
文献类型：
--
作者：
Masato Ishii;Takashi Takenouchi;Masashi Sugiyama
我们解决了部分零镜头设置下的域适应问题。在这种情况下，未标记的目标数据中缺少某个类子集，而所有类都出现在标记的源数据中，目标是区分目标域中的所有类。为了解决这个问题，我们利用对抗训练方案，并采用实例加权来估计与缺失类中不可用目标数据相关的损失。实例权重是基于深度神经网络的预测计算的，这意味着哪个实例将类似于看不见的数据，并且具有用于损失估计的有用信息。这种估计使得即使在部分零触发设置中，也可以在域自适应训练期间显式地考虑所有类，这导致域之间的准确自适应。在多个基准数据集上的实验结果验证了该方法的优越性。
We tackle a domain adaptation problem under partially zero-shot setting. In this setting, a certain subset of classes is missing in the unlabeled target data, while all classes appear in the labeled source data, and the goal is to discriminate all classes at the target domain. To solve this problem, we utilize an adversarial training scheme and adopt instance weighting to estimate the loss related to unavailable target data in the missing classes. The instance weight is computed on the basis of the prediction of deep neural networks, implying which instance would be similar to unseen data and having useful information for the loss estimation. This estimation makes it possible to explicitly consider all classes during the domain adaptation training even in the partially zero-shot setting, which leads to accurate adaptation between domains. Experimental results with several benchmark datasets validate the advantage of our method.