Open-world Machine Learning: Applications, Challenges, and Opportunities

Abstract

Traditional machine learning, mainly supervised learning, follows the assumptions of closed-world learning, i.e., for each testing class, a training class is available. However, such machine learning models fail to identify the classes, which were not available during training time. These classes can be referred to asunseen classes. Open-world Machine Learning (OWML) is a novel technique, which deals with unseen classes. Although OWML is around for a few years and many significant research works have been carried out in this domain, there is no comprehensive survey of the characteristics, applications, and impact of OWML on the major research areas. In this article, we aimed to capture the different dimensions of OWML with respect to other traditional machine learning models. We have thoroughly analyzed the existing literature and provided a novel taxonomy of OWML considering its two major application domains: Computer Vision and Natural Language Processing. We listed the available software packages and open datasets in OWML for future researchers. Finally, the article concludes with a set of research gaps, open challenges, and future directions.