Searching Similarity Measure for Binarized Neural Networks

• URL: http://arxiv.org/abs/2206.03325v1
• Date: Sun, 5 Jun 2022 06:53:53 GMT
• Title: Searching Similarity Measure for Binarized Neural Networks
• Authors: Yanfei Li, Ang Li, Huimin Yu
• Abstract summary: Binarized Neural Networks (BNNs) are a promising model to be deployed in resource-limited devices. BNNs suffer from non-trivial accuracy degradation, limiting its applicability in various domains. We propose an automatic searching method, based on genetic algorithm, for BNN-tailored similarity measure.
• Score: 14.847148292246374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry. However, comparing to the full-precision deep neural networks (DNNs), BNNs suffer from non-trivial accuracy degradation, limiting its applicability in various domains. This is partially because existing network components, such as the similarity measure, are specially designed for DNNs, and might be sub-optimal for BNNs. In this work, we focus on the key component of BNNs -- the similarity measure, which quantifies the distance between input feature maps and filters, and propose an automatic searching method, based on genetic algorithm, for BNN-tailored similarity measure. Evaluation results on Cifar10 and Cifar100 using ResNet, NIN and VGG show that most of the identified similarty measure can achieve considerable accuracy improvement (up to 3.39%) over the commonly-used cross-correlation approach.

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