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

TL;DR

This paper introduces OGRS, a gradient-based method for online training of deep neural networks that effectively detects and utilizes clean data in the presence of noisy labels, improving robustness and performance.

Contribution

It proposes a novel online gradient-based approach for robust deep learning with noisy labels, capable of automatically selecting clean samples without prior knowledge of data quality.

Findings

Outperforms state-of-the-art methods in various noisy label scenarios.

Theoretical proof of convergence to low-loss regions.

Effective in online streaming data environments.

Abstract

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. During the training process, the OGRS method selects clean samples at each iteration and feeds the selected sample to incrementally update the model parameters. We provide a detailed theoretical analysis to demonstrate data selection process is converging to the low-loss region of the sample space, by introducing and proving the sub-linear local Lagrangian regret of the non-convex constrained optimization problem. Experimental results show that it outperforms state-of-the-art methods in different settings.