SAB: Self-Adaptive Bias

Abstract

Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples with less consistent predictions and automatically determine the warming-up period based on the characteristics of the data. Additionally, to mitigate learning bias, we introduced a variable that adjusts the range of sample selection according to the progress of the training. To validate our method, we conducted extensive experiments on both balanced and imbalanced data classification tasks, and our proposed approach showed an average improvement of about 1.8%, with a maximum improvement of up to 4.4%, compared to previously suggested methods.