  It is crucial to distinguish mislabeled samples for dealing with noisy
labels. Previous methods such as Coteaching and JoCoR introduce two different
networks to select clean samples out of the noisy ones and only use these clean
ones to train the deep models. Different from these methods which require to
train two networks simultaneously, we propose a simple and effective method to
identify clean samples only using one single network. We discover that the
clean samples prefer to reach consistent predictions for the original images
and the transformed images while noisy samples usually suffer from inconsistent
predictions. Motivated by this observation, we introduce to constrain the
transform consistency between the original images and the transformed images
for network training, and then select small-loss samples to update the
parameters of the network. Furthermore, in order to mitigate the negative
influence of noisy labels, we design a classification loss by using the
off-line hard labels and on-line soft labels to provide more reliable
supervisions for training a robust model. We conduct comprehensive experiments
on CIFAR-10, CIFAR-100 and Clothing1M datasets. Compared with the baselines, we
achieve the state-of-the-art performance. Especially, in most cases, our
proposed method outperforms the baselines by a large margin.
