Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects

The article introduces a Confusion-Aware Transfer Teacher Curriculum Learning Framework that disentangles scoring and pacing effects in curriculum learning. It evaluates a confusion-aware difficulty score on CIFAR-10 using ResNet-18 and VGG-16, demonstrating improved data efficiency with curriculum ordering over random ordering, achieving up to an 8.7% accuracy increase at the 20% data regime. This framework highlights the importance of effective scoring functions and curriculum strategies for enhancing training efficiency in AI models.