  Despite deep learning has achieved great success, it often relies on a large
amount of training data with accurate labels, which are expensive and
time-consuming to collect. A prominent direction to reduce the cost is to learn
with noisy labels, which are ubiquitous in the real-world applications. A
critical challenge for such a learning task is to reduce the effect of network
memorization on the falsely-labeled data. In this work, we propose an iterative
selection approach based on the Weibull mixture model, which identifies clean
data by considering the overall learning dynamics of each data instance. In
contrast to the previous small-loss heuristics, we leverage the observation
that deep network is easy to memorize and hard to forget clean data. In
particular, we measure the difficulty of memorization and forgetting for each
instance via the transition times between being misclassified and being
memorized in training, and integrate them into a novel metric for selection.
Based on the proposed metric, we retain a subset of identified clean data and
repeat the selection procedure to iteratively refine the clean subset, which is
finally used for model training. To validate our method, we perform extensive
experiments on synthetic noisy datasets and real-world web data, and our
strategy outperforms existing noisy-label learning methods.
