OpenCML: End-to-End Framework of Open-world Machine Learning to Learn Unknown Classes Incrementally

TL;DR

This paper introduces OpenCML, an end-to-end framework for open-world machine learning that incrementally learns unknown classes, enabling continual learning and outperforming existing methods in open-world scenarios.

Contribution

It presents a novel framework combining class discovery and incremental learning, advancing open-world machine learning capabilities.

Findings

Achieved an average accuracy of 82.54% over four iterations.

Successfully discovered and learned new classes in an open environment.

Outperformed existing approaches in open-world learning tasks.

Abstract

Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence systems must learn about novel classes and previously known tasks. The proposed model offers novel learning classes in an open and continuous learning environment. It consists of two different but connected tasks. First, it discovers unknown classes in the data and creates novel classes; next, it learns how to perform class incrementally for each new class. Together, they enable continual learning, allowing the system to expand its understanding of the data and improve over time. The proposed model also outperformed existing approaches in open-world learning. Furthermore, it demonstrated strong performance in continuous learning, achieving a highest average accuracy of 82.54% over four iterations and a minimum accuracy of 65.87%.