Intra Class Distribution

Intra-class distribution refers to the variability within a single class of data, a crucial aspect in machine learning impacting model accuracy and generalization. Current research focuses on improving model robustness to this variability, particularly in imbalanced datasets, by developing techniques that enhance intra-class compactness while maintaining inter-class separation. This involves exploring novel loss functions, sampling strategies (like curriculum learning and flexible sampling), and generative models to better capture the inherent structure within classes. Addressing intra-class distribution challenges is vital for improving the performance and reliability of machine learning models across diverse applications, including anomaly detection and medical image classification.