Open-set classification is a problem of handling 'unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns. In contrast, we train networks for joint classification and reconstruction of input data. This enhances the learned representation so as to preserve information useful for separating unknowns from knowns, as well as to discriminate classes of knowns. Our novel Classification-Reconstruction learning for Open-Set Recognition (CROSR) utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy. Extensive experiments reveal that the peoposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers.
开集分类是一种处理训练数据集中不包含“未知”类的问题，然而，传统分类器假设，只有已知类出现在测试环境中。现有的开集分类器依赖于深度网络，该网络是以监督方式在已知类训练集中训练的；这就会导致，学习表示，对已知类的特化，并且使其难以区分已知和未知。相反，我们我们训练网络以进行输入数据的联合分类和重建。这增强了学习的表示，以便保存用于区分未知和已知的信息，同时对已知分类。
我们有一种新的分类-重建学习（CROSR），针对开集识别，使用潜在表示进行重建，并且能够在不损害已知级分类准确度的情况下实现稳健的未知检测。大量实验表明，我们提出的方法在多个标准数据集中优于现有的深度开集分类器，并且对各种异常具有鲁棒性。
