We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space. Our approach, called Open Set Translation and Adaptation Network (OSTAN), consists of two main components: translation and adaptation. The translation is a cycle-consistent generative adversarial network, which translates any source image to the “style” of a target domain to eliminate domain discrepancy in the pixel space. The adaptation is an instance-weighted adversarial network, which projects both (labeled) translated source images and (unlabeled) target images into a domain-invariant feature space to learn a prior probability for each target image. The learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown class. The proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets. Our experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.

我们建议通过在像素空间和特征空间上对齐图像来解决开放集域适应问题。我们的方法称为开放集翻译和适应网络（OSTAN），由两个主要部分组成：翻译和适应。翻译是一个循环一致的生成对抗网络，它将任何源图像转换为目标域的“风格”，以消除像素空间中的域差异。该适应是一个实例加权对抗网络，它将（标记的）翻译源图像和（未标记的）目标图像投影到域不变特征空间中，以学习每个目标图像的先验概率。将学习到的概率作为权重应用于未知分类器，以方便识别未知类别。所提出的 OSTAN 模型在多个公共数据集上显着优于最先进的开放集域适应方法。我们的实验还表明，图像到图像的转换和实例加权框架都可以进一步改善已知和未知类的决策边界。