New Data Processing Module Makes Deep Neural Networks Smarter

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power. "Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. "But they have mostly been treated separately. We found that combining them made them more efficient and effective."