image classification, metric learning, unseen class detection, open-world classification

The typical classification task is based on the assumption that the model will later only encounter examples of classes available during its training. In practice, this is often not a realistic assumption, because of limitations in obtaining enough labeled training data. This contribution is focused on the case where the model might encounter a sample belonging to a class different from the classes seen in the training phase. The goal is to reject examples of unseen classes with the option of later adding them as representatives of new classes without the need to retrain the backbone model. This is important because the end-user might not be able to re-train the model for any reason. The presented approach is based on metric learning combined with the meta-classifier similar to the approach of Xu et al. [1]. Classified examples are first embedded in a vector space through an encoder trained to capture similarities in the input data. The classification itself is then performed by 𝑛, where 𝑛 is the number of known classes, binary decisions. For each decision, the tested example is compared to the 𝑘 closest examples from the given class. If the model does not decide that the example belongs to any class, this example is rejected as possibly unknown. The method is tested in a visual data classification task.