Margin-based Sampling in Deep Metric Learning
Authors: 
Shangwei Zhou, Qingsong Yu, Jun SunAuthors Info & Claims
Pages 277 - 280
Abstract
Deep metric learning is routinely trained with a pair or a triplet of data samples, either of which converges slowly. It is usually to adopt hard negative mining for a fast convergence. Many existing methods train with the hardest examples, which are selected by ranking or scoring the hardness of examples, and are computationally expensive. In this paper, we propose a sampling strategy for hard negative mining, in which sampling directions are determined by the similarity between data examples and the margin. Our proposed method reduces the impact of noise by amplifying the dissimilarity of Softmax loss between hard examples for a more accurate model. The experimental results of image retrieval on CARS196 and CUB-200-2011 dataset demonstrate the effectiveness and superiority of our proposed method, compared to several existing state-of-the-art techniques.
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Published In
May 2019
353 pages
ISBN:9781450362788
DOI:10.1145/3335484
Copyright © 2019 ACM.
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- Shenzhen University: Shenzhen University
- Sun Yat-Sen University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 May 2019
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ICBDC 2019
ICBDC 2019: 2019 4th International Conference on Big Data and Computing
May 10 - 12, 2019
Guangzhou, China
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