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Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.
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Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2020
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Learning with bounded instance-and label-dependent label noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, and Dacheng Tao · 2020
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Sigua: Forgetting may make learning with noisy labels more robust
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Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
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Class2simi: A new perspective on learning with label noise
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Label-noise robust domain adaptation
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