论文标题
条件分类：降低计算能量的解决方案
Conditional Classification: A Solution for Computational Energy Reduction
论文作者
论文摘要
深度卷积神经网络在计算机视觉和其他应用中表现出很高的效率。但是，随着网络深度的增加，计算复杂性呈指数增长。在本文中，我们提出了一种新颖的解决方案，以减少用于许多类图像分类的卷积神经网络模型的计算复杂性。我们提出的技术将分类任务分为两个步骤：1）粗粒分类，其中输入样品分为一组超级类别，2）细粒分类，其中在第一步中检测到的那些超级类别中预测了最终标签。我们说明，我们所提出的分类器只能通过激活图像分类所需的模型的部分来达到最佳的类分类模型中最佳的班级分类模型报告的准确性水平。
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed technique breaks the classification task into two steps: 1) coarse-grain classification, in which the input samples are classified among a set of hyper-classes, 2) fine-grain classification, in which the final labels are predicted among those hyper-classes detected at the first step. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.
