Learning Deep Networks from Noisy Labels with Dropout Regularization

9 May 2017Ishan JindalMatthew NoklebyXuewen Chen

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks... (read more)

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