The triplet loss is adopted by a variety of learning tasks, such as local feature descriptor learning. However, its standard formulation with a hard margin only leverages part of the training data in each mini-batch. Moreover, the margin is often empirically chosen or determined through computationally expensive validation, and stays unchanged during the entire training session. In this work, we propose a simple yet effective method to overcome the above limitations. The core idea is to replace the hard margin with a non-parametric soft margin, which is dynamically updated. The major observation is that the difficulty of a triplet can be inferred from the cumulative distribution function of the triplets’ signed distances to the decision boundary. We demonstrate through experiments on both real-valued and binary local feature descriptors that our method leads to state-of-the-art performance on popular benchmarks, while eliminating the need to determine the best margin.

@inproceedings{Zhang:2019:LLD,
   author = "Linguang Zhang and Szymon Rusinkiewicz",
   title = "Learning Local Descriptors with a {CDF}-Based Dynamic Soft Margin",
   booktitle = "International Conference on Computer Vision (ICCV) oral presentation",
   year = "2019",
   month = oct
}