In this paper, we focus on a general yet important learning problem, pairwise
similarity learning (PSL). PSL subsumes a wide range of important applications,
such as open-set face recognition, speaker verification, image retrieval and
person re-identification. The goal of PSL is to learn a pairwise similarity
function assigning a higher similarity score to positive pairs (i.e., a pair of
samples with the same label) than to negative pairs (i.e., a pair of samples
with different label). We start by identifying a key desideratum for PSL, and
then discuss how existing methods can achieve this desideratum. We then propose
a surprisingly simple proxy-free method, called SimPLE, which requires neither
feature/proxy normalization nor angular margin and yet is able to generalize
well in open-set recognition. We apply the proposed method to three challenging
PSL tasks: open-set face recognition, image retrieval and speaker verification.
Comprehensive experimental results on large-scale benchmarks show that our
method performs significantly better than current state-of-the-art methods.

在本文中，我们研究了一个广泛且重要的学习问题 —— 成对相似度学习（PSL）。我们提出了一种简单但令人惊讶的无代理方法 SimPLE，该方法在开集识别中能够很好地推广，应用于开集人脸识别、图像检索和说话人验证三个挑战性的 PSL 任务上，通过大规模基准实验结果表明我们的方法明显优于现有的最先进方法。