  Large-scale datasets may contain significant proportions of noisy (incorrect)
class labels, and it is well-known that modern deep neural networks (DNNs)
poorly generalize from such noisy training datasets. To mitigate the issue, we
propose a novel inference method, termed Robust Generative classifier (RoG),
applicable to any discriminative (e.g., softmax) neural classifier pre-trained
on noisy datasets. In particular, we induce a generative classifier on top of
hidden feature spaces of the pre-trained DNNs, for obtaining a more robust
decision boundary. By estimating the parameters of generative classifier using
the minimum covariance determinant estimator, we significantly improve the
classification accuracy with neither re-training of the deep model nor changing
its architectures. With the assumption of Gaussian distribution for features,
we prove that RoG generalizes better than baselines under noisy labels.
Finally, we propose the ensemble version of RoG to improve its performance by
investigating the layer-wise characteristics of DNNs. Our extensive
experimental results demonstrate the superiority of RoG given different
learning models optimized by several training techniques to handle diverse
scenarios of noisy labels.
