论文标题
将果断性和鲁棒性指标应用于卷积神经网络
Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks
论文作者
论文摘要
我们回顾了三个最近提供的分类器质量指标，并考虑其对大规模分类挑战的适用性，例如将卷积神经网络应用于1000级Imagenet数据集。这些指标，称为“几何精度”，“果断性”和“鲁棒性”，基于分类器自我报告和测量的正确分类概率的普遍平均值（分别等于0、1和-2/3）。我们还提出了一些较小的澄清，以标准化度量定义。通过这些更新，我们显示了一些用深卷积神经网络（Alexnet和Densenet）作用在大型数据集（德国交通标志识别基准和Imagenet）上计算指标的示例。
We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset. These metrics, referred to as the "geometric accuracy," "decisiveness," and "robustness," are based on the generalized mean ($ρ$ equals 0, 1, and -2/3, respectively) of the classifier's self-reported and measured probabilities of correct classification. We also propose some minor clarifications to standardize the metric definitions. With these updates, we show some examples of calculating the metrics using deep convolutional neural networks (AlexNet and DenseNet) acting on large datasets (the German Traffic Sign Recognition Benchmark and ImageNet).
