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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Safeguarded Dynamic Label Regression for Noisy Supervision

February 1, 2023

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Authors

Jiangchao Yao

Shanghai Jiao Tong University


Hao Wu

Shanghai Jiao Tong University


Ya Zhang

Shanghai Jiao Tong University


Ivor W. Tsang

University of Technology Sydney


Jun Sun

Shanghai Jiao Tong University


DOI:

10.1609/aaai.v33i01.33019103


Abstract:

Learning with noisy labels is imperative in the Big Data era since it reduces expensive labor on accurate annotations. Previous method, learning with noise transition, has enjoyed theoretical guarantees when it is applied to the scenario with the class-conditional noise. However, this approach critically depends on an accurate pre-estimated noise transition, which is usually impractical. Subsequent improvement adapts the preestimation in the form of a Softmax layer along with the training progress. However, the parameters in the Softmax layer are highly tweaked for the fragile performance and easily get stuck into undesired local minimums. To overcome this issue, we propose a Latent Class-Conditional Noise model (LCCN) that models the noise transition in a Bayesian form. By projecting the noise transition into a Dirichlet-distributed space, the learning is constrained on a simplex instead of some adhoc parametric space. Furthermore, we specially deduce a dynamic label regression method for LCCN to iteratively infer the latent true labels and jointly train the classifier and model the noise. Our approach theoretically safeguards the bounded update of the noise transition, which avoids arbitrarily tuning via a batch of samples. Extensive experiments have been conducted on controllable noise data with CIFAR10 and CIFAR-100 datasets, and the agnostic noise data with Clothing1M and WebVision17 datasets. Experimental results have demonstrated that the proposed model outperforms several state-of-the-art methods.

Topics: AAAI

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HOW TO CITE:

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun Safeguarded Dynamic Label Regression for Noisy Supervision Proceedings of the AAAI Conference on Artificial Intelligence (2019) 9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun Safeguarded Dynamic Label Regression for Noisy Supervision AAAI 2019, 9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun (2019). Safeguarded Dynamic Label Regression for Noisy Supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. Safeguarded Dynamic Label Regression for Noisy Supervision. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. 2019. Safeguarded Dynamic Label Regression for Noisy Supervision. "Proceedings of the AAAI Conference on Artificial Intelligence". 9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. (2019) "Safeguarded Dynamic Label Regression for Noisy Supervision", Proceedings of the AAAI Conference on Artificial Intelligence, p.9103-9110

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun, "Safeguarded Dynamic Label Regression for Noisy Supervision", AAAI, p.9103-9110, 2019.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. "Safeguarded Dynamic Label Regression for Noisy Supervision". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. "Safeguarded Dynamic Label Regression for Noisy Supervision". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 9103-9110.

Jiangchao Yao||Hao Wu||Ya Zhang||Ivor W. Tsang||Jun Sun. Safeguarded Dynamic Label Regression for Noisy Supervision. AAAI[Internet]. 2019[cited 2023]; 9103-9110.


ISSN: 2374-3468


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