Pairwise Similarity Learning is SimPLE
Yandong Wen Weiyang Liu Yao Feng Bhiksha Raj Rita Singh Adrian Weller Michael J. Black Bernhard Schölkopf
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.