Difficulty Based Weighting

Difficulty-based weighting in machine learning aims to improve model training by assigning different weights to training samples based on their perceived difficulty. Current research focuses on developing robust and theoretically grounded measures of sample difficulty, encompassing factors like noise, class imbalance, and decision boundary uncertainty, often within the context of semi-supervised learning and medical image analysis. This approach seeks to address limitations of standard training methods by mitigating biases stemming from imbalanced datasets and improving model generalization performance, ultimately leading to more accurate and reliable machine learning models across various applications.