  Training a deep neural network heavily relies on a large amount of training
data with accurate annotations. To alleviate this problem, various methods have
been proposed to annotate the data automatically. However, automatically
generating annotations will inevitably yields noisy labels. In this paper, we
propose a Data Selection and joint Training (DST) method to automatically
select training samples with accurate annotations. Specifically, DST fits a
mixture model according to the original annotation as well as the predicted
label for each training sample, and the mixture model is utilized to
dynamically divide the training dataset into a correctly labeled dataset, a
correctly predicted set and a wrong dataset. Then, DST is trained with these
datasets in a supervised manner. Due to confirmation bias problem, we train the
two networks alternately, and each network is tasked to establish the data
division to teach another network. For each iteration, the correctly labeled
and predicted labels are reweighted respectively by the probabilities from the
mixture model, and a uniform distribution is used to generate the probabilities
of the wrong samples. Experiments on CIFAR-10, CIFAR-100 and Clothing1M
demonstrate that DST is the comparable or superior to the state-of-the-art
methods.
