  Due to the memorization effect in Deep Neural Networks (DNNs), training with
noisy labels usually results in inferior model performance. Existing
state-of-the-art methods primarily adopt a sample selection strategy, which
selects small-loss samples for subsequent training. However, prior literature
tends to perform sample selection within each mini-batch, neglecting the
imbalance of noise ratios in different mini-batches. Moreover, valuable
knowledge within high-loss samples is wasted. To this end, we propose a
noise-robust approach named Jo-SRC (Joint Sample Selection and Model
Regularization based on Consistency). Specifically, we train the network in a
contrastive learning manner. Predictions from two different views of each
sample are used to estimate its "likelihood" of being clean or
out-of-distribution. Furthermore, we propose a joint loss to advance the model
generalization performance by introducing consistency regularization. Extensive
experiments have validated the superiority of our approach over existing
state-of-the-art methods.
