Open-world Machine Learning: A Review and New Outlooks

arXiv:2403.01759v1 Announce Type: new
Abstract: Machine learning has achieved remarkable success in many applications. However, existing studies are largely based on the closed-world assumption, which assumes that the environment is stationary, and the model is fixed once deployed. In many real-world applications, this fundamental and rather naive assumption may not hold because an open environment is complex, dynamic, and full of unknowns. In such cases, rejecting unknowns, discovering novelties, and then incrementally learning them, could enable models to be safe …

abstract applications arxiv cs.cv cs.lg environment machine machine learning open-world review studies success the environment type world