论文标题
是时候进行背景检查了！发现背景特征对深神经网络的影响
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
论文作者
论文摘要
随着表达能力的增加，深层神经网络已显着改善了图像分类数据集（例如ImageNet）的最新技术。在本文中，我们调查了深层神经网络的性能在多大程度上受到背景特征的影响？特别是，我们专注于背景不变性，即准确性不受转换背景特征和背景影响的影响，即当掩盖前景时背景特征的预测能力本身。我们使用32个不同的神经网络进行实验，从小型网络到训练多达十亿张图像的大型网络。我们的研究表明，DNN的表达能力的增加会导致背景特征的更高影响，同时同时提高了当删除背景特征或用随机选择的基于纹理的背景替代背景特征时，可以提高其做出正确的预测的能力。
With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we investigate to what extent the increasing performance of deep neural networks is impacted by background features? In particular, we focus on background invariance, i.e., accuracy unaffected by switching background features and background influence, i.e., predictive power of background features itself when foreground is masked. We perform experiments with 32 different neural networks ranging from small-size networks to large-scale networks trained with up to one Billion images. Our investigations reveal that increasing expressive power of DNNs leads to higher influence of background features, while simultaneously, increases their ability to make the correct prediction when background features are removed or replaced with a randomly selected texture-based background.
