  Deep learning models can perform well when evaluated on images from the same
distribution as the training set. However, applying small perturbations in the
forms of noise, artifacts, occlusions, blurring, etc. to a model's input image
and feeding the model with out-of-distribution (OOD) data can significantly
drop the model's accuracy, making it not applicable to real-world scenarios.
Data augmentation is one of the well-practiced methods to improve model
robustness against OOD data; however, examining which augmentation type to
choose and how it affects the OOD robustness remains understudied. There is a
growing belief that augmenting datasets using data augmentations that improve a
model's bias to shape-based features rather than texture-based features results
in increased OOD robustness for Convolutional Neural Networks trained on the
ImageNet-1K dataset. This is usually stated as ``an increase in the model's
shape bias results in an increase in its OOD robustness". Based on this
hypothesis, some works in the literature aim to find augmentations with higher
effects on model shape bias and use those for data augmentation. By evaluating
39 types of data augmentations on a widely used OOD dataset, we demonstrate the
impact of each data augmentation on the model's robustness to OOD data and
further show that the mentioned hypothesis is not true; an increase in shape
bias does not necessarily result in higher OOD robustness. By analyzing the
results, we also find some biases in the ImageNet-1K dataset that can easily be
reduced using proper data augmentation. Our evaluation results further show
that there is not necessarily a trade-off between in-domain accuracy and OOD
robustness, and choosing the proper augmentations can help increase both
in-domain accuracy and OOD robustness simultaneously.
