Justifiable Granularity

Justifiable granularity focuses on optimizing the level of detail (granularity) used in data representation and analysis to improve model performance and efficiency across various applications. Current research explores this concept in diverse fields, including image segmentation, machine learning classification, robot control via language, and knowledge graph-based data annotation, employing techniques like granular-ball classifiers, hierarchical segmentation, and the development of novel loss functions for optimal granularity selection. This research is significant because appropriately chosen granularity enhances model accuracy, reduces computational costs, and improves the interpretability of results across numerous domains, from assistive robotics to text-to-image retrieval.