In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC learning has the ability to impose constraints on the shape of
the feature distributions, and thus the generalization ability is improved. BC
learning is originally a method developed for sounds, which can be digitally
mixed. Mixing two image data does not appear to make sense; however, we argue
that because convolutional neural networks have an aspect of treating input
data as waveforms, what works on sounds must also work on images. First, we
propose a simple mixing method using internal divisions, which surprisingly
proves to significantly improve performance. Second, we propose a mixing method
that treats the images as waveforms, which leads to a further improvement in
performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
ImageNet-1K and CIFAR-10, respectively.

本文介绍了一种新的图像分类学习方法，称之为 Between-Class learning (BC learning)，通过两个不同类别的图像以随机的比例混合生成新的图像，并训练模型输出混合的比例，从而使模型的泛化能力得到提高。同时，提出了基于内部分割和将图像视为波形的两种混合方法，实验表明，在 ImageNet-1K 和 CIFAR-10 两个数据集上，该方法能够实现很好的分类效果。