基于联合对抗训练的鲁棒度量迁移

doi:10.12066/j.issn.1007-2861.2460

Abstract

Abstract:

Transfer metric learning (TML) aims to improve the metric learning in target domains by transferring knowledge from those related tasks where the distance metrics are strong and reliable. Existing TML approaches focus on only transferring the source metric knowledge, which is often prone to overfitting to the source domain. In this study, we train a source metric that is appropriate for transfer and then design a general deep TML method for effective metric transfer. In particular, we propose learning the source metric parameterized by a deep neural network through joint adversarial training and then transferring the metric to the target domain by embedding imitation, which allows the inputs of source and target domains to be heterogeneous. Besides, we restrict the size of the target metric network to be small so that the inference is efficient in the target domain. Finally, the results of applying the proposed method to a popular face verification application demonstrate its effectiveness.

Key words: transfer metric learning (TML), deep metric learning, joint adversarial training, heterogeneous domain

CLC Number:

TP 37

YANG Qiancheng, LUO Yong, HU Han, ZHOU Xin, DU Bo, TAO Dacheng. Robust metric transfer using joint adversarial training[J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 1-9.

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References 22

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Robust metric transfer using joint adversarial training

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