  While mislabeled or ambiguously-labeled samples in the training set could
negatively affect the performance of deep models, diagnosing the dataset and
identifying mislabeled samples helps to improve the generalization power.
Training dynamics, i.e., the traces left by iterations of optimization
algorithms, have recently been proved to be effective to localize mislabeled
samples with hand-crafted features. In this paper, beyond manually designed
features, we introduce a novel learning-based solution, leveraging a noise
detector, instanced by an LSTM network, which learns to predict whether a
sample was mislabeled using the raw training dynamics as input. Specifically,
the proposed method trains the noise detector in a supervised manner using the
dataset with synthesized label noises and can adapt to various datasets (either
naturally or synthesized label-noised) without retraining. We conduct extensive
experiments to evaluate the proposed method. We train the noise detector based
on the synthesized label-noised CIFAR dataset and test such noise detector on
Tiny ImageNet, CUB-200, Caltech-256, WebVision and Clothing1M. Results show
that the proposed method precisely detects mislabeled samples on various
datasets without further adaptation, and outperforms state-of-the-art methods.
Besides, more experiments demonstrate that the mislabel identification can
guide a label correction, namely data debugging, providing orthogonal
improvements of algorithm-centric state-of-the-art techniques from the data
aspect.
