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[bibtex]@InProceedings{Zhang_2021_ICCV, author = {Zhang, Yaobin and Deng, Weihong and Zhong, Yaoyao and Hu, Jiani and Li, Xian and Zhao, Dongyue and Wen, Dongchao}, title = {Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {15065-15075} }
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
Abstract
The training of a deep face recognition system usually faces the interference of label noise in the training data. However, it is difficult to obtain a high-precision cleaning model to remove these noises. In this paper, we propose an adaptive label noise cleaning algorithm based on meta-learning for face recognition datasets, which can learn the distribution of the data to be cleaned and make automatic adjustments based on class differences. It first learns reliable cleaning knowledge from well-labeled noisy data, then gradually transfers it to the target data with meta-supervision to improve performance. A threshold adapter module is also proposed to address the drift problem in transfer learning methods. Extensive experiments clean two noisy in-the-wild face recognition datasets and show the effectiveness of the proposed method to reach state-of-the-art performance on the IJB-C face recognition benchmark.
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