Abstract
Traditional machine learning especially 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 as unseen classes. Whereas, open-world machine learning deals with arbitrary inputs (data with unseen classes) to machine learning systems. Moreover, traditional machine learning is static learning which is not appropriate for an active environment where the perspective and sources, and/or volume of data are changing rapidly. In this paper, first, we present an overview of open-world learning with importance to the real-world context. Next, different dimensions of open-world learning are explored and discussed. The area of open-world learning gained the attention of the research community in the last decade only. We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for open-world machine learning. It also presents the research gaps, challenges, and future directions in open-world learning. This paper will help researchers to understand the comprehensive developments of open-world learning and the likelihoods to extend the research in suitable areas. It will also help to select applicable methodologies and datasets to explore this further.
Abstract (translated)
URL
https://arxiv.org/abs/2105.13448
