A problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, three of which are proposed here for the first time. These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT). Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature.

本研究提出了基于数据层面上的集成学习方法，在 Convolutional Neural Networks 的训练集上应用将14种数据增强方法生成的图像构建ensemble，其中包括了基于傅里叶变换、Radon 变换和离散余弦变换的三种新型数据增强方法。通过11个基准测试的比较，证明了该方法不仅与当前的最优方法竞争力度一致，有时还能超过其性能。

图像数据增强的特征转换