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.