Class Variability

Class variability, the diversity within a given data class, is a significant challenge in machine learning, impacting model accuracy and robustness. Current research focuses on mitigating the effects of high intra-class variability (differences within a class) and low inter-class variability (similarities between classes) through techniques like data condensation, dimension reduction in Wasserstein metric space, and adaptive weighting schemes during training. These advancements aim to improve classification performance in various applications, including image quality assessment, object detection, and zero-shot learning, by enhancing feature separability and reducing the impact of noisy or ambiguous data. The resulting improvements in model accuracy and generalization have significant implications for diverse fields, from medical image analysis to agricultural applications.