불확실한 환경에서의 이미지 분류 성능 향상을 위한 Mix Channel Split 데이터 증강 기법

We propose a new data augmentation method that works by separating the RGB channels of an image to improve image classification ability in uncertain environments. Many data augmentation methods, using technique such as flipping and cropping, have been used to improve the image classification ability of models. while these data augmentation methods have been effective in improving image classification, they have unperformed in uncertain conditions. To solve this problem, we propose the ChannelSplit that separates and reassembles the RGB channels of an image, along with the Mix ChannelSplit, that adopts the concept of MixUp[1,2] to express more diversity. In this paper, the proposed ChannelSplit and Mix ChannelSplit are called ChannelAug because they only utilize channels and do not perform any other image operations. Also, we compare ChannelAug to other image augmentation methods to prove it enhances robustness and uncertainty measures on image classification.