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Today's available datasets in the wild, e.g., from social media and openplatforms, present tremendous opportunities and challenges for deep learning,as there is a significant portion of tagged images, but often with noisy, i.e.erroneous, labels. Recent studies improve the robustness of deep models againstnoisy labels without the knowledge of true labels. In this paper, we advocateto derive a stronger classifier which proactively makes use of the noisy labelsin addition to the original images - turning noisy labels into learningfeatures. To such an end, we propose a novel framework, ExpertNet, composed ofAmateur and Expert, which iteratively learn from each other. Amateur is aregular image classifier trained by the feedback of Expert, which imitates howhuman experts would correct the predicted labels from Amateur using the noisepattern learnt from the knowledge of both the noisy and ground truth labels.The trained Amateur and Expert proactively leverage the images and their noisylabels to infer image classes. Our empirical evaluations on noisy versions ofCIFAR-10, CIFAR-100 and real-world data of Clothing1M show that the proposedmodel can achieve robust classification against a wide range of noise ratiosand with as little as 20-50% training data, compared to state-of-the-art deepmodels that solely focus on distilling the impact of noisy labels.

ExpertNet: Adversarial Learning and Recovery Against Noisy Labels