Colorful Cutout: Enhancing Image Data Augmentation with Curriculum
  Learning
Juhwan Choi YoungBin Kim
Data augmentation is one of the regularization strategies for the training of
deep learning models, which enhances generalizability and prevents overfitting,
leading to performance improvement. Although researchers have proposed various
data augmentation techniques, they often lack consideration for the difficulty
of augmented data. Recently, another line of research suggests incorporating
the concept of curriculum learning with data augmentation in the field of
natural language processing. In this study, we adopt curriculum data
augmentation for image data augmentation and propose colorful cutout, which
gradually increases the noise and difficulty introduced in the augmented image.
Our experimental results highlight the possibility of curriculum data
augmentation for image data. We publicly released our source code to improve
the reproducibility of our study.