Mathematics-Inspired Models: A Green and Interpretable Learning Paradigm for Multimedia Computing
Lei Gao, Kai Liu, Zheng Guo, Ling Guan · ACM Transactions on Multimedia Computing Communications and Applications · 2025
The advances of machine learning (ML), and AI in general, have attracted unprecedented attention in intelligent multimedia computing and many other fields. However, due to the concern for sustainability and black-box nature of ML models, especially deep neural networks (DNNs), green and interpretable learnings have been extensively studied in recent years, despite suspicions on effectiveness, subjectivity of interpretability, and complexity. To address these concerns and suspicions, this article starts with a survey on recent discoveries in green learning and interpretable learning and then presents mathematics-inspired (M-I) learning models. We will demonstrate that the M-I models are green in nature with numerous interpretable properties. Finally, we present several examples in multi-view information computing on both static image-based and dynamic video-based tasks to demonstrate that the M-I methodology promises a plausible and sustainable path for natural evolution of ML, which is worth further investment in.