title: Training deep neural-networks using a noise adaptation layer
abstract: The availability of large datsets has enabled neural networks to achieve impressive recognition results. However, the presence of inaccurate class labels is known  to deteriorate the performance of even the best classifiers in a broad range of classification problems. Noisy labels also  tend to be more harmful than noisy attributes. When the observed label is noisy, we can view the correct label as a latent random variable and model the noise processes by a communication channel with unknown parameters. Thus we can apply the EM algorithm to find the parameters of both the network and the noise  and to estimate the correct label. In this study we present a neural-network approach that optimizes  the same likelihood function as optimized by the EM algorithm. The noise is explicitly modeled by an additional softmax layer that connects the correct labels to the noisy ones. This scheme is then extended  to the case where the noisy labels are dependent  on the features in addition to the correct labels.  Experimental results demonstrate that this approach  outperforms previous methods.
  
authors: Jacob Goldberger Ehud Ben-Reuven
hIndex: Jacob Goldberger 40