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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets

February 1, 2023

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Authors

Yingsong Huang

Tencent


Bing Bai

Tencent


Shengwei Zhao

Tencent


Kun Bai

Tencent


Fei Wang

Cornell University


DOI:

10.1609/aaai.v36i6.20654


Abstract:

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks.Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples.These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world.We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., inter-class loss distribution discrepancy and misleading predictions due to uncertainty.The first issue is that existing methods often perform class-agnostic noise modeling. However, loss distributions show a significant discrepancy among classes under class imbalance, and class-agnostic noise modeling can easily get confused with noisy samples and samples in minority classes.The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples. Inspired by our observations, we propose an Uncertainty-aware Label Correction framework(ULC) to handle label noise on imbalanced datasets. First, we perform epistemic uncertainty-aware class-specific noise modeling to identify trustworthy clean samples and refine/discard highly confident true/corrupted labels.Then, we introduce aleatoric uncertainty in the subsequent learning process to prevent noise accumulation in the label noise modeling process. We conduct experiments on several synthetic and real-world datasets. The results demonstrate the effectiveness of the proposed method, especially on imbalanced datasets.

Topics: AAAI

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HOW TO CITE:

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets AAAI 2022, 6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang (2022). Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets. Proceedings of the AAAI Conference on Artificial Intelligence, 6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. 2022. Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets. "Proceedings of the AAAI Conference on Artificial Intelligence". 6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. (2022) "Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets", Proceedings of the AAAI Conference on Artificial Intelligence, p.6960-6969

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang, "Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets", AAAI, p.6960-6969, 2022.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. "Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. "Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6960-6969.

Yingsong Huang||Bing Bai||Shengwei Zhao||Kun Bai||Fei Wang. Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets. AAAI[Internet]. 2022[cited 2023]; 6960-6969.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
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