Light-Weight Permutation Generator for Efficient Convolutional Neural Network Data Augmentation

Abstract

Permutation is a fundamental way of data augmentation. However, it is not commonly used in image based systems with hardware acceleration due to distortion of spatial correlation and generation complexity. This paper proposes Restricted Permutation Network (RPN), a scalable architecture to automatically generate a restricted subset of local permutation, preserving the features of the dataset while simplifying the generation to improve scalability. RPN reduces the spatial complexity from $\textit{O}(Nlog(N))$ to $\textit{O}(N)$, making it easily scalable to 64 inputs and beyond, with 21 times speed up in generation and significantly reducing data storage and transfer, while maintaining the same level of accuracy as the original dataset for deep learning training. Experiments show Convolutional Neural Networks (CNNs) trained by the augmented dataset can be as accurate as the original one. Combining three to five networks in general improves the network accuracy by 5%. Network training can be accelerated by training multiple sub-networks in parallel with a reduced training data set and epochs, resulting in up to 5 times speed up with a negligible loss in accuracy. This opens up the opportunity to easily split long iterative training process into independent parallelizable processes, facilitating the trade off between resources and run time.