Guest Editorial: Beyond Classic Deep Learning: Algorithms for Dealing with Real-World Applications in Industrial Automation

In the rapidly evolving landscape of Industry 4.0 and the forthcoming Industry 5.0, the integration of intelligent systems into industrial automation has become a cornerstone for achieving efficiency, adaptability, and sustainability. In particular, deep learning (DL) has been central to this transformation, by empowering machines to extract meaningful patterns from complex, high-dimensional data, DL has demonstrated remarkable success in tackling industrial challenges such as anomaly detection, predictive maintenance, quality control, and process optimization However, as industrial systems become increasingly interconnected and dynamic, classic supervised learning paradigms often fail to meet the practical demands of real-world environments and operate under assumptions that are frequently violated in real-world industrial deployments.