Improving Neural Network with Uniform Sparse Connectivity

Abstract

Neural network forms the foundation of deep learning and numerous AI applications. Classical neural networks are fully connected, expensive to train and prone to overfitting. Sparse networks tend to have convoluted structure search, suboptimal performance and limited usage. We proposed the novel uniform sparse network (USN) with even and sparse connectivity within each layer. USN has one striking property that its performance is independent of the substantial topology variation and enormous model space, thus offers a search-free solution to all above mentioned issues of neural networks. USN consistently and substantially outperforms the state-of-the-art sparse network models in prediction accuracy, speed and robustness. It even achieves higher prediction accuracy than the fully connected network with only 0.55% parameters and 1/4 computing time and resources. Importantly, USN is conceptually simple as a natural generalization of fully connected network with multiple improvements in accuracy, robustness and scalability. USN can replace the latter in a range of applications, data types and deep learning architectures. We have made USN open source at https://github.com/datapplab/sparsenet.

paper accepted by IEEE Access

References in corpus (8)

• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Recurrent Neural Network Regularization
• To prune, or not to prune: exploring the efficacy of pruning for model compression
• Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
• DropBlock: A regularization method for convolutional networks
• A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
• Sparse Networks from Scratch: Faster Training without Losing Performance
• Deep Rewiring: Training very sparse deep networks