  Deep neural networks are often not robust to semantically-irrelevant changes
in the input. In this work we address the issue of robustness of
state-of-the-art deep convolutional neural networks (CNNs) against commonly
occurring distortions in the input such as photometric changes, or the addition
of blur and noise. These changes in the input are often accounted for during
training in the form of data augmentation. We have two major contributions:
First, we propose a new regularization loss called feature-map augmentation
(FMA) loss which can be used during finetuning to make a model robust to
several distortions in the input. Second, we propose a new combined
augmentations (CA) finetuning strategy, that results in a single model that is
robust to several augmentation types at the same time in a data-efficient
manner. We use the CA strategy to improve an existing state-of-the-art method
called stability training (ST). Using CA, on an image classification task with
distorted images, we achieve an accuracy improvement of on average 8.94% with
FMA and 8.86% with ST absolute on CIFAR-10 and 8.04% with FMA and 8.27% with ST
absolute on ImageNet, compared to 1.98% and 2.12%, respectively, with the well
known data augmentation method, while keeping the clean baseline performance.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning