Noise-resistant Deep Metric Learning with Ranking-based Instance
Selection
- URL: http://arxiv.org/abs/2103.16047v1
- Date: Tue, 30 Mar 2021 03:22:17 GMT
- Title: Noise-resistant Deep Metric Learning with Ranking-based Instance
Selection
- Authors: Chang Liu and Han Yu and Boyang Li and Zhiqi Shen and Zhanning Gao and
Peiran Ren and Xuansong Xie and Lizhen Cui and Chunyan Miao
- Abstract summary: We propose a noise-resistant training technique for DML, which we name Probabilistic Ranking-based Instance Selection with Memory (PRISM)
PRISM identifies noisy data in a minibatch using average similarity against image features extracted from several previous versions of the neural network.
To alleviate the high computational cost brought by the memory bank, we introduce an acceleration method that replaces individual data points with the class centers.
- Score: 59.286567680389766
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The existence of noisy labels in real-world data negatively impacts the
performance of deep learning models. Although much research effort has been
devoted to improving robustness to noisy labels in classification tasks, the
problem of noisy labels in deep metric learning (DML) remains open. In this
paper, we propose a noise-resistant training technique for DML, which we name
Probabilistic Ranking-based Instance Selection with Memory (PRISM). PRISM
identifies noisy data in a minibatch using average similarity against image
features extracted by several previous versions of the neural network. These
features are stored in and retrieved from a memory bank. To alleviate the high
computational cost brought by the memory bank, we introduce an acceleration
method that replaces individual data points with the class centers. In
extensive comparisons with 12 existing approaches under both synthetic and
real-world label noise, PRISM demonstrates superior performance of up to 6.06%
in Precision@1.
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