Smart Augmentation - Learning an Optimal Data Augmentation Strategy

Understand

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(DNN).

• 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 overfitting 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.