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 highconfidence 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, CALFW, CPLFW, AgeDB, CFP, RFW, and MegaFace, have demonstrated the effectiveness of our new approach over the stateof-the-art alternatives. Our code is available at http: //www.cbsr.ia.ac.cn/users/xiaobowang/.
Dataset - https://drive.google.com/uc?id=1Of_EVz-yHV7QVWQGihYfvtny9Ne8qXVz&export=download https://academictorrents.com/details/9e67eb7cc23c9417f39778a8e06cca5e26196a97/tech&hit=1&filelist=1 http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/