Title: Significance of Softmax-Based Features over Metric Learning-Based Features
Authors: Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa
Keywords: Computer vision, Deep learning
TL;DR: We show softmax-based features are markedly better than state-of-the-art metric learning-based features by conducting fair comparison between them.
Abstract:
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.
End-to-end DML approaches such as Magnet Loss and lifted structured feature embedding show state-of-the-art performance in several image recognition tasks.
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 are markedly better than the state-of-the-art DML features for tasks such as fine-grained recognition, attribute estimation, clustering, and retrieval.
Venue: Submitted to ICLR 2017