ACL: active curriculum learning to reducing label efforts

Abstract

Annotation is a labor-intensive task in deep learning, which requires large amounts of training data. In active learning, which reduces the annotation work, the performance of the model is improved without annotating all the data by performing annotation step by step. In this study, we propose a method to incorporate a curriculum learning framework into active learning, which improves the performance of the model by learning from samples that are easy to identify. The experimental results show that the proposed method achieves 20% reduction in the total annotations compared to random sampling on CIFAR-10.