The extraction of useful deep features is important for many computer vision tasks. Deep features extracted from classification networks have proved to perform well in those tasks. To obtain features of greater usefulness, end-to-end distance metric learning (DML) has been applied to train the feature extractor directly. However, in these DML studies, there were no equitable comparisons between features extracted from a DML-based network and those from a softmax-based network. In this paper, by presenting objective comparisons between these two approaches under the same network architecture, we show that the softmax-based features perform competitive, or even better, to the state-of-the-art DML features when the size of the dataset, that is, the number of training samples per class, is large. The results suggest that softmax-based features should be properly taken into account when evaluating the performance of deep features.
翻译:摘取有用的深层特征对于许多计算机的愿景任务十分重要。从分类网络中提取的深层特征证明在这些任务中表现良好。为了获得更有用的特征,直接应用端到端的远程计量学习(DML)来培训特征提取器。然而,在这些DML研究中,从基于DML的网络中提取的特征与基于软式的网络中提取的特征之间没有进行公平的比较。在本文件中,通过在同一网络架构下对这两种方法进行客观比较,我们表明,在数据集的大小,即每类培训样本的数量很大的情况下,基于软式的特征在评估深层特征的性能时,应当适当考虑基于软式特征。