  In particular, the lack of sufficient amounts of domain-specific data can
reduce the accuracy of a classifier. In this paper, we explore the effects of
style transfer-based data transformation on the accuracy of a convolutional
neural network classifiers in the context of automobile detection under adverse
winter weather conditions. The detection of automobiles under highly adverse
weather conditions is a difficult task as such conditions present large amounts
of noise in each image. The InceptionV2 architecture is trained on a composite
dataset, consisting of either normal car image dataset , a mixture of normal
and style transferred car images, or a mixture of normal car images and those
taken at blizzard conditions, at a ratio of 80:20. All three classifiers are
then tested on a dataset of car images taken at blizzard conditions and on
vehicle-free snow landscape images. We evaluate and contrast the effectiveness
of each classifier upon each dataset, and discuss the strengths and weaknesses
of style-transfer based approaches to data augmentation.
