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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Learning from Noisy Labels with Complementary Loss Functions

February 1, 2023

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Authors

Deng-Bao Wang

Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education


Yong Wen

Noah’s Ark Lab, Huawei Technologies


Lujia Pan

Noah’s Ark Lab, Huawei Technologies NSKEYLAB, Xi’an Jiaotong University


Min-Ling Zhang

Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education Collaborative Innovation Center of Wireless Communications Technology


DOI:

10.1609/aaai.v35i11.17213


Abstract:

Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. On the other hand, the commonly used Cross Entropy (CE) loss, which shows high performance in standard supervised learning (with clean supervision), is non-robust to label noise. In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties respectively. Specifically, we find that by exploiting the memorization effect of neural networks, we can easily filter out a proportion of hard samples and generate reliable pseudo labels for easy samples, and thus reduce the label noise to a quite low level. Then, we simply learn with CE on pseudo supervision and robust loss on original noisy supervision. In this procedure, CE can guarantee the sufficiency of optimization while the robust loss can be regarded as the supplement. Experimental results on benchmark classification datasets indicate that the proposed method helps achieve robust and sufficient deep neural network training simultaneously.

Topics: AAAI

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HOW TO CITE:

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang Learning from Noisy Labels with Complementary Loss Functions Proceedings of the AAAI Conference on Artificial Intelligence (2021) 10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang Learning from Noisy Labels with Complementary Loss Functions AAAI 2021, 10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang (2021). Learning from Noisy Labels with Complementary Loss Functions. Proceedings of the AAAI Conference on Artificial Intelligence, 10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. Learning from Noisy Labels with Complementary Loss Functions. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. 2021. Learning from Noisy Labels with Complementary Loss Functions. "Proceedings of the AAAI Conference on Artificial Intelligence". 10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. (2021) "Learning from Noisy Labels with Complementary Loss Functions", Proceedings of the AAAI Conference on Artificial Intelligence, p.10111-10119

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang, "Learning from Noisy Labels with Complementary Loss Functions", AAAI, p.10111-10119, 2021.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. "Learning from Noisy Labels with Complementary Loss Functions". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. "Learning from Noisy Labels with Complementary Loss Functions". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 10111-10119.

Deng-Bao Wang||Yong Wen||Lujia Pan||Min-Ling Zhang. Learning from Noisy Labels with Complementary Loss Functions. AAAI[Internet]. 2021[cited 2023]; 10111-10119.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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