论文标题
Chimeramix：通过蒙版特征混合在小数据集上的图像分类
ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
论文作者
论文摘要
深卷积神经网络需要大量标记的数据样本。对于许多实际应用，这是一个主要限制，通常通过增强方法对待。在这项工作中，我们解决了在小数据集上学习深神经网络的问题。我们提出的称为Chimeramix的架构通过生成实例组成来学习数据的增强。生成模型成对编码图像，结合了由面具引导的功能，并创建了新样本。为了进行评估，所有方法均已从头开始训练，没有任何其他数据。基准数据集上的几个实验，例如CIFAIR-10，STL-10和CIFAIR-100与当前用于小型数据集分类的最新方法相比，Chimeramix的出色表现。
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets.
