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.
翻译:革命神经网络(CNNs)的一个问题是,它们需要大型数据集才能获得足够的稳健性;小数据集很容易被过度配置。提出了许多方法来克服有线电视新闻网的这一缺陷。在无法轻易收集更多样本的情况下,通常的做法是使用增强技术从现有数据中产生更多的数据点。在图像分类中,许多增强方法使用简单的图像操纵算法。在这项工作中,我们通过添加十四种增强方法生成的图像,其中三种是首次在这里提出。这些新方法以Fourier变换、Radon变换和Discrete Cosine变换为基础。预先训练的ResNet50网络对包含从每种增强方法中获得的图像的培训组进行了微调。这些网络和若干组合组合在11个基准中进行了评估和比较。结果显示,通过将不同的数据增强方法合并在数据级别上建立组合,不仅与状态竞争,而且往往超过文献中报告的最佳方法。