Multi-proxy based deep metric learning

Abstract

Deep metric learning (DML) achieves excellent performance in many open-set scenarios. However, the current multi-proxy methods rely on a classification framework and the performances of these methods rely on large batch size due to the representation limitation of mini-batch for neighbor distribution of proxies. This study proposes a multi-proxy method aiming to enhance the generalization ability of DML on unseen classes by enhancing the data representation ability. As a sample may not share all fine-grained semantic information of a class, our model does not require full association between samples and proxies, and is updated only according to the associated pairs. Moreover, a mini-batch may not contain sufficient information for learning. So our model also considers the proxy-proxy relation in order to provide a global view of the data structure which improves the learning on the intra-class distance. This study also investigates how the positive and negative margins, i.e., the parameters of our model which control the required similarity for a class and different classes, affect our performance, and finds that less demanding on the negative margin avoids over-training and improves generalization ability to unseen classes. The outstanding performance of our model is confirmed by experimental results compared with the state-of-the-art DML methods in the benchmark image retrieval datasets.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.