Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes

Understand

Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning.

• Methods that generate mixed data from multiple data sets, such as mixup, can acquire new diversity that is not included in the training data, and thus contribute significantly to accuracy improvement.
• However, since the data selected for mixing are randomly sampled throughout the training process, there are cases where appropriate classes or data are not selected.
• In this study, we propose a data augmentation method that calculates the distance between classes based on class probabilities and can select data from suitable classes to be mixed in the training process.