  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. Mixture data is dynamically adjusted according to the
training trend of each class to facilitate training. The proposed method is
applied in combination with conventional methods for generating mixed data.
Evaluation experiments show that the proposed method improves recognition
performance on general and long-tailed image recognition datasets.
