论文标题
搜索相似性衡量二进制神经网络
Searching Similarity Measure for Binarized Neural Networks
论文作者
论文摘要
作为一个有前途的模型，要在资源有限的设备中部署，二进制的神经网络（BNN）引起了学术和行业的广泛关注。但是，与完整精确的深神经网络（DNN）相比，BNNS遭受了非平凡的精度降解，从而限制了其在各个领域的适用性。这部分是因为现有的网络组件（例如相似性度量）是专门为DNN设计的，并且可能是BNN的优势。 在这项工作中，我们专注于BNN的关键组成部分 - 相似性度量，量化了输入特征图和过滤器之间的距离，并提出了基于遗传算法的自动搜索方法，以实现BNN量化的相似性度量。使用Resnet，NIN和VGG对CIFAR10和CIFAR100进行评估结果表明，大多数已识别的类似措施可以比常用的互相关方法实现相当大的准确性改善（高达3.39％）。
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.
