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Xian-Jin Gui

2 accepted papers

2021

Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion

IJCAI 2021poster

Deep neural networks need large amounts of labeled data to achieve good performance. In real-world applications, labels are usually collected from non-experts such as crowdsourcing to save cost and thus are noisy. In the past few years, deep learning methods for dealing with noisy labels have been d…

Cited by 60SourcePDFScholar
2020

Towards Accurate and Robust Domain Adaptation under Noisy Environments

IJCAI 2020poster

In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation works have achieved great success in theory and practice. However, they always lose robustness in noisy environments where t…