SAFE-DNN: A Deep Neural Network With Spike Assisted Feature Extraction For Noise Robust Inference

被引：1

作者：

She, Xueyuan ^{[1
]}

Saha, Priyabrata ^{[1
]}

Kim, Daehyun ^{[1
]}

Long, Yun ^{[1
]}

Mukhopadhyay, Saibal ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

关键词：

deep learning; noise robustness; spiking neural network; spike-timing-dependent plasticity (STDP);

D O I：

10.1109/ijcnn48605.2020.9207274

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs. The proposed network augments a DNN with unsupervised learning of low-level features using spiking neural network (SNN) with spike-timing-dependent plasticity (STDP). The complete network learns to ignore local perturbation while performing global feature detection and classification. The experimental results on CIFAR-10 and ImageNet subset demonstrate improved noise robustness for multiple DNN architectures without sacrificing accuracy on clean images.

引用

页数：8

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