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Robustness to label noise is a critical property for weakly-supervisedclassifiers trained on massive datasets. Robustness to label noise is acritical property for weakly-supervised classifiers trained on massivedatasets. In this paper, we first derive analytical bound for any given noisepatterns. Based on the insights, we design TrustNet that first adversely learnsthe pattern of noise corruption, being it both symmetric or asymmetric, from asmall set of trusted data. Then, TrustNet is trained via a robust lossfunction, which weights the given labels against the inferred labels from thelearned noise pattern. The weight is adjusted based on model uncertainty acrosstraining epochs. We evaluate TrustNet on synthetic label noise for CIFAR-10 andCIFAR-100, and real-world data with label noise, i.e., Clothing1M. We compareagainst state-of-the-art methods demonstrating the strong robustness ofTrustNet under a diverse set of noise patterns.

TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise