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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

February 1, 2023

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Authors

Xuchao Zhang

Virginia Tech


Xian Wu

University of Notre Dame


Fanglan Chen

Virginia Tech


Liang Zhao

George Mason University


Chang-Tien Lu

Virginia Tech


DOI:

10.1609/aaai.v34i04.6166


Abstract:

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of well-labeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.

Topics: AAAI

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HOW TO CITE:

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data Proceedings of the AAAI Conference on Artificial Intelligence (2020) 6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data AAAI 2020, 6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu (2020). Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. Proceedings of the AAAI Conference on Artificial Intelligence, 6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. 2020. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. "Proceedings of the AAAI Conference on Artificial Intelligence". 6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. (2020) "Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data", Proceedings of the AAAI Conference on Artificial Intelligence, p.6853-6860

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu, "Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data", AAAI, p.6853-6860, 2020.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. "Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. "Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 6853-6860.

Xuchao Zhang||Xian Wu||Fanglan Chen||Liang Zhao||Chang-Tien Lu. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. AAAI[Internet]. 2020[cited 2023]; 6853-6860.


ISSN: 2374-3468


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