Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
This addresses the challenge of handling emerging unobserved classes in data streams for applications like image and text analysis, representing an incremental improvement over prior similarity-based methods.
The paper tackles the problem of detecting novel classes in data streams, especially with high-dimensional data like images and texts, by proposing a semi-supervised multi-task learning framework that learns a latent space for classification and novel class detection, demonstrating superiority over existing methods in empirical evaluations on real-world datasets.
One of the key challenges of performing label prediction over a data stream concerns with the emergence of instances belonging to unobserved class labels over time. Previously, this problem has been addressed by detecting such instances and using them for appropriate classifier adaptation. The fundamental aspect of a novel-class detection strategy relies on the ability of comparison among observed instances to discriminate them into known and unknown classes. Therefore, studies in the past have proposed various metrics suitable for comparison over the observed feature space. Unfortunately, these similarity measures fail to reliably identify distinct regions in observed feature spaces useful for class discrimination and novel-class detection, especially in streams containing high-dimensional data instances such as images and texts. In this paper, we address this key challenge by proposing a semi-supervised multi-task learning framework called \sysname{} which aims to intrinsically search for a latent space suitable for detecting labels of instances from both known and unknown classes. We empirically measure the performance of \sysname{} over multiple real-world image and text datasets and demonstrate its superiority by comparing its performance with existing semi-supervised methods.