论文标题
CNN是否编码数据增强？
Do CNNs Encode Data Augmentations?
论文作者
论文摘要
数据增强是培训强大神经网络的配方中的重要成分，尤其是在计算机视觉中。一个基本问题是神经网络是否具有编码数据增强转换。为了回答这个问题，我们介绍了一种系统的方法来研究哪些神经网络的层次是增强转化的最终预测。我们的方法在预训练的视觉模型中使用特征，并具有最小的附加处理，以预测通过增强（尺度，纵横比，色调，饱和度，对比度和亮度）转化的共同特性。令人惊讶的是，神经网络不仅可以预测数据增强转换，而且还可以高精度预测许多转换。在验证了与增强转换相对应的神经网络编码功能之后，我们表明这些特征是在现代CNN的早期层中编码的，尽管增强信号在更深的层中逐渐消失。
Data augmentations are important ingredients in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network features encode data augmentation transformations. To answer this question, we introduce a systematic approach to investigate which layers of neural networks are the most predictive of augmentation transformations. Our approach uses features in pre-trained vision models with minimal additional processing to predict common properties transformed by augmentation (scale, aspect ratio, hue, saturation, contrast, and brightness). Surprisingly, neural network features not only predict data augmentation transformations, but they predict many transformations with high accuracy. After validating that neural networks encode features corresponding to augmentation transformations, we show that these features are encoded in the early layers of modern CNNs, though the augmentation signal fades in deeper layers.
