Abstract
As machine learning techniques increase in complexity, their hunger for more training data is ever-growing. Deep learning for image recognition is no exception. In some domains, training images are expensive or difficult to collect. When training image availability is limited, researchers naturally turn to synthetic methods of generating new imagery for training. We evaluate several methods of training data augmentation in the context of improving performance of a Convolutional Neural Network (CNN) in the domain of fine-grain aircraft classification. We conclude that randomly scaling training imagery significantly improves performance. Also, we find that drawing random occlusions on top of training images confers a similar improvement in our problem domain. Further, we find that these two effects seem to be approximately additive, with our results demonstrating a 45.7% reduction in test error over basic horizontal flipping and cropping.
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Acknowledgements
This work was supported by the Air Force Research Lab’s Sensors Directorate, Layered Sensing Exploitation Division. The views expressed in this work are those of the authors, and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government. This document has been approved for public release.
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Mash, R., Borghetti, B., Pecarina, J. (2016). Improved Aircraft Recognition for Aerial Refueling Through Data Augmentation in Convolutional Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_11
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DOI: https://doi.org/10.1007/978-3-319-50835-1_11
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