A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels

Understand

Learning with noisy labels is an important topic for scalable training in many real-world scenarios.

• However, few previous research considers this problem in the online setting, where the arrival of data is streaming.
• In this paper, we propose a novel gradient-based approach to enable the detection of noisy labels for the online learning of model parameters, named Online Gradient-based Robust Selection (OGRS).
• In contrast to the previous sample selection approach for the offline training that requires the estimation of a clean ratio of the dataset before each epoch of training, OGRS can automatically select clean samples by steps of gradient update from datasets with varying clean ratios without changing the parameter setting.