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Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 62 state-of-the-art robust training methods, all of which are categorized into five groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we perform an in-depth analysis of noise rate estimation and summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies.
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Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G. (2022). Learning From Noisy Labels With Deep Neural Networks: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 8135–8153. https://doi.org/10.1109/tnnls.2022.3152527
Song, Hwanjun, et al. "Learning From Noisy Labels With Deep Neural Networks: A Survey." IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, 2022, pp. 8135–8153, https://doi.org/10.1109/tnnls.2022.3152527.
Song, Hwanjun, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. "Learning From Noisy Labels With Deep Neural Networks: A Survey." IEEE Transactions on Neural Networks and Learning Systems 34, no. 11 (2022): 8135–8153. https://doi.org/10.1109/tnnls.2022.3152527.
Song H., Kim M., Park D. [и др.] Learning From Noisy Labels With Deep Neural Networks: A Survey // IEEE Transactions on Neural Networks and Learning Systems. — 2022. — Vol. 34, № 11. — P. 8135–8153. — URL: https://doi.org/10.1109/tnnls.2022.3152527
@article{song2022learning,
title = {{Learning From Noisy Labels With Deep Neural Networks: A Survey}},
author = {Hwanjun Song and Minseok Kim and Dongmin Park and Yooju Shin and Jae-Gil Lee},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2022},
volume = {34},
number = {11},
pages = {8135--8153},
doi = {10.1109/tnnls.2022.3152527},
url = {https://doi.org/10.1109/tnnls.2022.3152527}
}TY - JOUR AU - Song, Hwanjun AU - Kim, Minseok AU - Park, Dongmin AU - Shin, Yooju AU - Lee, Jae-Gil TI - Learning From Noisy Labels With Deep Neural Networks: A Survey JO - IEEE Transactions on Neural Networks and Learning Systems VL - 34 IS - 11 SP - 8135 EP - 8153 PY - 2022 DA - 2022/03/07 DO - 10.1109/tnnls.2022.3152527 UR - https://doi.org/10.1109/tnnls.2022.3152527 ER -
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