论文标题
基于拉普拉斯频谱下的累积最大规模区域的数据集复杂性评估
Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum
论文作者
论文摘要
数据集复杂性评估旨在在训练分类器之前先预测具有复杂性计算的数据集上的分类性能，该分类器也可以用于分类器选择和减少数据集。深卷积神经网络（DCNN）的训练过程是迭代的且耗时的，这是由于高参数不确定性和不同数据集引入的域移位。因此，通过在培训DCNN模型之前有效评估数据集的复杂性来预测分类性能是有意义的。本文提出了一种新的方法，称为Laplacian Spectrum（CMSAUL）下的称为累积最大缩放区域，该方法可以在六个数据集上实现最新的复杂性评估性能。
Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifier selection and dataset reduction. The training process of deep convolutional neural networks (DCNNs) is iterative and time-consuming because of hyperparameter uncertainty and the domain shift introduced by different datasets. Hence, it is meaningful to predict classification performance by assessing the complexity of datasets effectively before training DCNN models. This paper proposes a novel method called cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS), which can achieve state-of-the-art complexity assessment performance on six datasets.
