Synaptic Strength For Convolutional Neural Network

arXiv.org Artificial Intelligence

Convolutional Neural Networks(CNNs) are both computation and memory intensive which hindered their deployment in mobile devices. Inspired by the relevant concept in neural science literature, we propose Synaptic Pruning: a data-driven method to prune connections between input and output feature maps with a newly proposed class of parameters called Synaptic Strength. Synaptic Strength is designed to capture the importance of a connection based on the amount of information it transports. Experiment results show the effectiveness of our approach. On CIFAR-10, we prune connections for various CNN models with up to 96% , which results in significant size reduction and computation saving. Further evaluation on ImageNet demonstrates that synaptic pruning is able to discover efficient models which is competitive to state-of-the-art compact CNNs such as MobileNet-V2 and NasNet-Mobile. Our contribution is summarized as following: (1) We introduce Synaptic Strength, a new class of parameters for CNNs to indicate the importance of each connections. (2) Our approach can prune various CNNs with high compression without compromising accuracy. (3) Further investigation shows, the proposed Synaptic Strength is a better indicator for kernel pruning compared with the previous approach in both empirical result and theoretical analysis.


Synaptic Strength For Convolutional Neural Network

Neural Information Processing Systems

Convolutional Neural Networks(CNNs) are both computation and memory inten-sive which hindered their deployment in mobile devices. Inspired by the relevantconcept in neural science literature, we propose Synaptic Pruning: a data-drivenmethod to prune connections between input and output feature maps with a newlyproposed class of parameters called Synaptic Strength. Synaptic Strength is de-signed to capture the importance of a connection based on the amount of informa-tion it transports. Experiment results show the effectiveness of our approach. OnCIFAR-10, we prune connections for various CNN models with up to96%, whichresults in significant size reduction and computation saving. Further evaluation onImageNet demonstrates that synaptic pruning is able to discover efficient modelswhich is competitive to state-of-the-art compact CNNs such as MobileNet-V2andNasNet-Mobile. Our contribution is summarized as following: (1) We introduceSynaptic Strength, a new class of parameters for CNNs to indicate the importanceof each connections. (2) Our approach can prune various CNNs with high com-pression without compromising accuracy. (3) Further investigation shows, theproposed Synaptic Strength is a better indicator for kernel pruning compared withthe previous approach in both empirical result and theoretical analysis.


Synaptic Strength For Convolutional Neural Network

Neural Information Processing Systems

Convolutional Neural Networks(CNNs) are both computation and memory inten-sive which hindered their deployment in mobile devices. Inspired by the relevantconcept in neural science literature, we propose Synaptic Pruning: a data-drivenmethod to prune connections between input and output feature maps with a newlyproposed class of parameters called Synaptic Strength. Synaptic Strength is de-signed to capture the importance of a connection based on the amount of informa-tion it transports. Experiment results show the effectiveness of our approach. OnCIFAR-10, we prune connections for various CNN models with up to96%, whichresults in significant size reduction and computation saving. Further evaluation onImageNet demonstrates that synaptic pruning is able to discover efficient modelswhich is competitive to state-of-the-art compact CNNs such as MobileNet-V2andNasNet-Mobile. Our contribution is summarized as following: (1) We introduceSynaptic Strength, a new class of parameters for CNNs to indicate the importanceof each connections. (2) Our approach can prune various CNNs with high com-pression without compromising accuracy. (3) Further investigation shows, theproposed Synaptic Strength is a better indicator for kernel pruning compared withthe previous approach in both empirical result and theoretical analysis.


Discrimination-aware Channel Pruning for Deep Neural Networks

Neural Information Processing Systems

Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. To overcome these drawbacks, we investigate a simple-yet-effective method, called discrimination-aware channel pruning, to choose those channels that really contribute to discriminative power. To this end, we introduce additional losses into the network to increase the discriminative power of intermediate layers and then select the most discriminative channels for each layer by considering the additional loss and the reconstruction error. Last, we propose a greedy algorithm to conduct channel selection and parameter optimization in an iterative way. Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels even outperforms the original model by 0.39% in top-1 accuracy.


Discrimination-aware Channel Pruning for Deep Neural Networks

Neural Information Processing Systems

Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. To overcome these drawbacks, we investigate a simple-yet-effective method, called discrimination-aware channel pruning, to choose those channels that really contribute to discriminative power. To this end, we introduce additional losses into the network to increase the discriminative power of intermediate layers and then select the most discriminative channels for each layer by considering the additional loss and the reconstruction error. Last, we propose a greedy algorithm to conduct channel selection and parameter optimization in an iterative way. Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels even outperforms the original model by 0.39% in top-1 accuracy.