Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering

• URL: http://arxiv.org/abs/2108.01431v1
• Date: Tue, 3 Aug 2021 12:15:25 GMT
• Title: Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering
• Authors: Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao
• Abstract summary: Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. We propose Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML. PRISM calculates the probability of a label being clean, and filters out potentially noisy samples.
• Score: 59.286567680389766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. Cleaning data manually is labour-intensive and time-consuming. Previous research mostly focuses on enhancing classification models against noisy labels, while the robustness of deep metric learning (DML) against noisy labels remains less well-explored. In this paper, we bridge this important gap by proposing Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML. PRISM calculates the probability of a label being clean, and filters out potentially noisy samples. Specifically, we propose three methods to calculate this probability: 1) Average Similarity Method (AvgSim), which calculates the average similarity between potentially noisy data and clean data; 2) Proxy Similarity Method (ProxySim), which replaces the centers maintained by AvgSim with the proxies trained by proxy-based method; and 3) von Mises-Fisher Distribution Similarity (vMF-Sim), which estimates a von Mises-Fisher distribution for each data class. With such a design, the proposed approach can deal with challenging DML situations in which the majority of the samples are noisy. Extensive experiments on both synthetic and real-world noisy dataset show that the proposed approach achieves up to 8.37% higher Precision@1 compared with the best performing state-of-the-art baseline approaches, within reasonable training time.

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