基于分离指数的迁移学习中预训练深度神经网络的排序和拒绝
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index
摘要：
预训练的深度神经网络（DNN）的自动排名减少了选择最佳预训练DNN所需的时间，并提高了迁移学习的分类性能。在本文中，我们介绍了一种新的算法，通过将一种简单的基于距离的复杂性度量（称为分离指数（SI））应用于目标数据集来对预训练的DNN进行排名。为此，首先，关于SI的背景，然后解释自动排序算法。在该算法中，SI是针对从预训练的DNN的特征提取部分通过的目标数据集计算的。然后，通过对计算出的SI进行降序排序，可以很容易地对预训练的DNN进行排名。在这种排名方法中，最好的DNN在目标数据集上产生最大的SI，并且在其计算的SI足够低的情况下，可以拒绝一些预先训练的DNN。通过使用三个具有挑战性的数据集，包括Linnaeus 5，乳腺癌图像和COVID-CT，评估所提出的算法的效率。对于前两个案例研究，所提出的算法的结果与目标数据集上训练的DNN的准确性完全匹配。对于第三个案例研究，尽管对目标数据进行了不同的预处理，但算法的排名与分类精度导致的排名具有很高的相关性。
英文摘要：
Automated ranking of pre-trained Deep Neural Networks (DNNs) reduces the required time for selecting optimal pre-trained DNN and boost the classification performance in transfer learning. In this paper, we introduce a novel algorithm to rank pre-trained DNNs by applying a straightforward distance-based complexity measure named Separation Index (SI) to the target dataset. For this purpose, at first, a background about the SI is given and then the automated ranking algorithm is explained. In this algorithm, the SI is computed for the target dataset which passes from the feature extracting parts of pre-trained DNNs. Then, by descending sort of the computed SIs, the pre-trained DNNs are ranked, easily. In this ranking method, the best DNN makes maximum SI on the target dataset and a few pre-trained DNNs may be rejected in the case of their sufficiently low computed SIs. The efficiency of the proposed algorithm is evaluated by using three challenging datasets including Linnaeus 5, Breast Cancer Images, and COVID-CT. For the two first case studies, the results of the proposed algorithm exactly match with the ranking of the trained DNNs by the accuracy on the target dataset. For the third case study, despite using different preprocessing on the target data, the ranking of the algorithm has a high correlation with the ranking resulted from classification accuracy.
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