Using instance hardness measures in curriculum learning

Abstract

Curriculum learning consists of training strategies for machine learning techniques in which the easiest observations are presented first, progressing into more difficult cases as training proceeds. For assembling the curriculum, it is necessary to order the observations a dataset has according to their difficulty. This work investigates how instance hardness measures, which can be used to assess the difficulty level of each observation in a dataset from different perspectives, can be used to assemble a curriculum. Experiments with four CIFAR-100 sub-problems have demonstrated the feasibility of using the instance hardness measures, the main advantage is on convergence speed and some datasets accuracy gains can also be verified.