12 citations · 13 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 1 cited
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Lefan Zhang, Zhang-Hao Tian, Wujun Zhou +1
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. Many methods have been…
cs.LG2021★ 12 cited
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion
Xian-Jin Gui, Wei Wang, Zhang-Hao Tian
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…