Learning Deep Networks from Noisy Labels with Dropout Regularization
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 underdetermined, 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.
2016 IEEE 16th International Conference on Data Mining (ICDM)