Robust and On-the-fly Dataset Denoising for Image Classification

TL;DR

This paper introduces On-the-fly Data Denoising (ODD), a method that models loss distributions to identify and remove noisy labels during training, improving robustness and generalization in image classification tasks.

Contribution

The paper proposes a novel loss distribution modeling approach for real-time removal of mislabeled examples, enhancing training robustness with minimal computational cost.

Findings

ODD achieves state-of-the-art results on WebVision and Clothing1M datasets.

The method effectively identifies noisy labels using loss distribution modeling.

ODD introduces almost no additional computational overhead.

Abstract

Memorization in over-parameterized neural networks could severely hurt generalization in the presence of mislabeled examples. However, mislabeled examples are hard to avoid in extremely large datasets collected with weak supervision. We address this problem by reasoning counterfactually about the loss distribution of examples with uniform random labels had they were trained with the real examples, and use this information to remove noisy examples from the training set. First, we observe that examples with uniform random labels have higher losses when trained with stochastic gradient descent under large learning rates. Then, we propose to model the loss distribution of the counterfactual examples using only the network parameters, which is able to model such examples with remarkable success. Finally, we propose to remove examples whose loss exceeds a certain quantile of the modeled loss distribution. This leads to On-the-fly Data Denoising (ODD), a simple yet effective algorithm that is robust to mislabeled examples, while introducing almost zero computational overhead compared to standard training. ODD is able to achieve state-of-the-art results on a wide range of datasets including real-world ones such as WebVision and Clothing1M.