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Smart Augmentation Learning an Optimal Data Augmentation Strategy


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Abstract


A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks. There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method, which we call smart augmentation and we show how to use it to increase the accuracy and reduce over tting on a target network. Smart augmentation works, by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart augmentation has shown the potential to increase accuracy by demonstrably signicant measures on all data sets tested. In addition, it has shown potential to achieve similar or improved performance levels with signicantly smaller network sizes in a number of tested cases.

KeyWords
Articial intelligence, articial neural networks, machine learning, computer vision supervised learning, machine learning algorithms, image databases.



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