Robust High Dimensional Stream Classification with Novel Class Detection

A primary challenge in label prediction over a data stream is the emergence of instances belonging to unknown or novel class over time. Traditionally, studies addressing this problem aim to detect such instances using cluster-based mechanisms. They typically assume that instances from the same class are closer to each other than those belonging to different classes in observed feature space. Unfortunately, this may not hold true in higher-dimensional feature space such as images. In recent years, Convolutional neural network (CNN) have emerged as a leading system to be employed in many real-world application. Yet, based on the assumption of closed world dataset with a fixed number of categories, CNN lacks robustness for novel class detection, so it is unclear on how such models can be used to deal with novel class instances along a high-dimensional image stream. In this paper, we focus on addressing this challenge by proposing an effective learning framework called CNN-based Prototype Ensemble (CPE) for novel class detection and correction. Our framework includes a prototype ensemble loss (PE) to improve the intra-class compactness and expand inter-class separateness in the output feature representation, thereby enabling the robustness of novel class detection. Moreover, we provide an incremental learning strategy which maintains a constant amount of exemplars to update the network, making it more practical for real-world application. We empirically demonstrate the effectiveness of our framework by comparing its performance over multiple realworld image benchmark data streams with existing state-of-theart data stream detection techniques. The implementation of CPE is on: https://github.com/Vitvicky/Convolutional-Net-PrototypeEnsemble