Title: Learning Deep Networks from Noisy Labels with Dropout Regularization,
Abstract: 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. We augment a standard deep network with a softmax layer that models
the label noise statistics. Then, we train the deep network and noise model
jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled)
dataset. The augmented model is overdetermined, so in order to encourage the
learning of a non-trivial noise model, we apply dropout regularization to the
weights of the noise model during training. Numerical experiments on noisy
versions of the CIFAR-10 and MNIST datasets show that the proposed dropout
technique outperforms state-of-the-art methods.