We show that combining human prior knowledge with end-to-end learning can
improve the robustness of deep neural networks by introducing a part-based
model for object classification. We believe that the richer form of annotation
helps guide neural networks to learn more robust features without requiring
more samples or larger models. Our model combines a part segmentation model
with a tiny classifier and is trained end-to-end to simultaneously segment
objects into parts and then classify the segmented object. Empirically, our
part-based models achieve both higher accuracy and higher adversarial
robustness than a ResNet-50 baseline on all three datasets. For instance, the
clean accuracy of our part models is up to 15 percentage points higher than the
baseline's, given the same level of robustness. Our experiments indicate that
these models also reduce texture bias and yield better robustness against
common corruptions and spurious correlations. The code is publicly available at
this https URL

本文介绍结合人类先验知识和端到端学习以提高深度神经网络的鲁棒性，并引入了一种面向目标分类的基于部件的模型，相较于基准模型 ResNet-50，我们的基于部件的模型在三种数据集上实验表明都具有更高的准确性和更高的适应性。