  We consider the problem of training a model under the presence of label
noise. Current approaches identify samples with potentially incorrect labels
and reduce their influence on the learning process by either assigning lower
weights to them or completely removing them from the training set. In the first
case the model however still learns from noisy labels; in the latter approach,
good training data can be lost. In this paper, we propose an iterative
semi-supervised mechanism for robust learning which excludes noisy labels but
is still able to learn from the corresponding samples. To this end, we add an
unsupervised loss term that also serves as a regularizer against the remaining
label noise. We evaluate our approach on common classification tasks with
different noise ratios. Our robust models outperform the state-of-the-art
methods by a large margin. Especially for very large noise ratios, we achieve
up to 20 % absolute improvement compared to the previous best model.
