MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels

Algan, Gorkem
Ulusoy, İlkay
Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. In each iteration, before conventional training, the meta-training loop updates soft-labels so that resulting gradients updates on the base classifier would yield minimum loss on meta-data. Soft-labels are generated from extracted features of data instances, and the mapping function is learned by a single layer perceptron (SLP) network, which is called MetaLabelNet. Following, base classifier is trained by using these generated soft-labels. These iterations are repeated for each batch of training data. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases. We perform extensive experiments on benchmark datasets with both synthetic and real-world noises. Results show that our approach outperforms existing baselines. The source code of the proposed model is available at
IEEE Transactions on Image Processing


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Citation Formats
G. Algan and İ. Ulusoy, “MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels,” IEEE Transactions on Image Processing, vol. 31, pp. 4352–4362, 2022, Accessed: 00, 2022. [Online]. Available: