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MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels
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Date
2022-01-01
Author
Algan, Gorkem
Ulusoy, İlkay
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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 https://github.com/gorkemalgan/MetaLabelNet.
Subject Keywords
Training
,
Noise measurement
,
Noise robustness
,
Feature extraction
,
Training data
,
Deep learning
,
Wide band gap semiconductors
,
Deep learning
,
label noise
,
noise robust
,
noise cleansing
,
meta-learning
,
SET
,
Deep learning
,
label noise
,
meta-learning
,
noise cleansing
,
noise robust
URI
https://hdl.handle.net/11511/99639
Journal
IEEE Transactions on Image Processing
DOI
https://doi.org/10.1109/tip.2022.3183841
Collections
Department of Electrical and Electronics Engineering, Article
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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: https://hdl.handle.net/11511/99639.