Self Paced

Self-paced learning (SPL) is a machine learning paradigm that mimics human learning by gradually increasing the complexity of training data, starting with easier examples and progressing to more challenging ones. Current research focuses on improving the efficiency and effectiveness of SPL, particularly through the development of adaptive algorithms that dynamically adjust the difficulty of training samples based on the model's performance, such as those employing adaptive mix-up techniques or generalized age-path algorithms. These advancements aim to enhance the performance and robustness of machine learning models across various applications, including medical image segmentation, by optimizing the learning process and reducing overfitting.