Co-Mining: Deep Face Recognition With Noisy Labels

Xiaobo Wang, Shuo Wang, Jun Wang, Hailin Shi, Tao Mei; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 9358-9367

Abstract


Face recognition has achieved significant progress with the growing scale of collected datasets, which empowers us to train strong convolutional neural networks (CNNs). While a variety of CNN architectures and loss functions have been devised recently, we still have a limited understanding of how to train the CNN models with the label noise inherent in existing face recognition datasets. To address this issue, this paper develops a novel co-mining strategy to effectively train on the datasets with noisy labels. Specifically, we simultaneously use the loss values as the cue to detect noisy labels, exchange the high-confidence clean faces to alleviate the errors accumulated issue caused by the sample-selection bias, and re-weight the predicted clean faces to make them dominate the discriminative model training in a mini-batch fashion. Extensive experiments by training on three popular datasets (i.e., CASIA-WebFace, MS-Celeb-1M and VggFace2) and testing on several benchmarks, including LFW, AgeDB, CFP, CALFW, CPLFW, RFW, and MegaFace, have demonstrated the effectiveness of our new approach over the state-of-the-art alternatives.

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[bibtex]
@InProceedings{Wang_2019_ICCV,
author = {Wang, Xiaobo and Wang, Shuo and Wang, Jun and Shi, Hailin and Mei, Tao},
title = {Co-Mining: Deep Face Recognition With Noisy Labels},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}