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Zhang-Hao Tian

2 accepted papers

2024

Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss

IJCAI 2024poster

The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Existing methods are usually dependent on label frequency to tackle class imbalance, while the model bias on different classes is not directly…

Cited by 3SourcePDFScholar
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