Stochastic Class-Based Hard Example Mining for Deep Metric Learning

Yumin Suh, Bohyung Han, Wonsik Kim, Kyoung Mu Lee; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7251-7259


Performance of deep metric learning depends heavily on the capability of mining hard negative examples during training. However, many metric learning algorithms often require intractable computational cost due to frequent feature computations and nearest neighbor searches in a large-scale dataset. As a result, existing approaches often suffer from trade-off between training speed and prediction accuracy. To alleviate this limitation, we propose a stochastic hard negative mining method. Our key idea is to adopt class signatures that keep track of feature embedding online with minor additional cost during training, and identify hard negative example candidates using the signatures. Given an anchor instance, our algorithm first selects a few hard negative classes based on the class-to-sample distances and then performs a refined search in an instance-level only from the selected classes. As most of the classes are discarded at the first step, it is much more efficient than exhaustive search while effectively mining a large number of hard examples. Our experiment shows that the proposed technique improves image retrieval accuracy substantially; it achieves the state-of-the-art performance on the several standard benchmark datasets.

Related Material

author = {Suh, Yumin and Han, Bohyung and Kim, Wonsik and Lee, Kyoung Mu},
title = {Stochastic Class-Based Hard Example Mining for Deep Metric Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}