Noise-Based and Class-Based Curriculum Learning for Image Classifiers

Datasets often contain different difficulty samples and even noisy samples. This paper introduces two naive curriculum learning methods, one using an image dataset with noise and another one using an image dataset that contains samples from other datasets with presumed higher difficulty. The final goal is to improve the performance of the model by gradually introducing more difficult samples during the training process rather than using them from the very beginning. Experiments demonstrated that using the proposed curriculum learning methods, a classifier can achieve higher accuracy in less training epochs.