  Data augmentation is widely used for machine learning; however, an effective
method to apply data augmentation has not been established even though it
includes several factors that should be tuned carefully. One such factor is
sample suitability, which involves selecting samples that are suitable for data
augmentation. A typical method that applies data augmentation to all training
samples disregards sample suitability, which may reduce classifier performance.
To address this problem, we propose the self-paced augmentation (SPA) to
automatically and dynamically select suitable samples for data augmentation
when training a neural network. The proposed method mitigates the deterioration
of generalization performance caused by ineffective data augmentation. We
discuss two reasons the proposed SPA works relative to curriculum learning and
desirable changes to loss function instability. Experimental results
demonstrate that the proposed SPA can improve the generalization performance,
particularly when the number of training samples is small. In addition, the
proposed SPA outperforms the state-of-the-art RandAugment method.
