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08/04/2021
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classif...
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11/13/2020
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Labeling real-world datasets is time consuming but indispensable for sup...
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07/13/2020
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Robustness to label noise is a critical property for weakly-supervised c...
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07/10/2020
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Today's available datasets in the wild, e.g., from social media and open...
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01/28/2020
