  Training deep neural networks (DNNs) in the presence of noisy labels is an
important and challenging task. Probabilistic modeling, which consists of a
classifier and a transition matrix, depicts the transformation from true labels
to noisy labels and is a promising approach. However, recent probabilistic
methods directly apply transition matrix to DNN, neglect DNN's susceptibility
to overfitting, and achieve unsatisfactory performance, especially under the
uniform noise. In this paper, inspired by label smoothing, we proposed a novel
method, in which a smoothed transition matrix is used for updating DNN, to
restrict the overfitting of DNN in probabilistic modeling. Our method is termed
Matrix Smoothing. We also empirically demonstrate that our method not only
improves the robustness of probabilistic modeling significantly, but also even
obtains a better estimation of the transition matrix.
