Informative Sample-Aware Proxy for Deep Metric Learning

• URL: http://arxiv.org/abs/2211.10382v1
• Date: Fri, 18 Nov 2022 17:25:25 GMT
• Title: Informative Sample-Aware Proxy for Deep Metric Learning
• Authors: Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota
• Abstract summary: In existing methods, a relatively small number of samples can produce large gradient magnitudes. We propose a novel proxy-based method called Informative Sample-Aware Proxy ( Proxy-ISA) It modifies a gradient weighting factor for each sample using a scheduled threshold function, so that the model is more sensitive to the informative samples.
• Score: 7.624717642858549
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding space, receive updates based on proxy-sample similarities in a similar manner to sample representations. In existing methods, a relatively small number of samples can produce large gradient magnitudes (ie, hard samples), and a relatively large number of samples can produce small gradient magnitudes (ie, easy samples); these can play a major part in updates. Assuming that acquiring too much sensitivity to such extreme sets of samples would deteriorate the generalizability of a method, we propose a novel proxy-based method called Informative Sample-Aware Proxy (Proxy-ISA), which directly modifies a gradient weighting factor for each sample using a scheduled threshold function, so that the model is more sensitive to the informative samples. Extensive experiments on the CUB-200-2011, Cars-196, Stanford Online Products and In-shop Clothes Retrieval datasets demonstrate the superiority of Proxy-ISA compared with the state-of-the-art methods.

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