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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 3Source ↗PDF ↗Scholar ↗
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 60Source ↗PDF ↗Scholar ↗