SUBM22 - Noisy-label Learning with Sample Selection based on Noise Rate Estimate
- 1 minNoisy-label Learning with Sample Selection based on Noise Rate Estimate
A Garg, C Nguyen, R Felix, TT Do, G Carneiro
Abstract
Noisy-labels are challenging for deep learning due to the high capacity of the deep models that can overfit noisy-label training samples. Arguably the most realistic and coincidentally challenging type of label noise is the instance-dependent noise (IDN), where the labelling errors are caused by the ambivalent information present in the images. The most successful label noise learning techniques to address IDN problems usually contain a noisy-label sample selection stage to separate clean and noisy-label samples during training. Such sample selection depends on a criterion, such as loss or gradient, and on a curriculum to define the proportion of training samples to be classified as clean at each training epoch. Even though the estimated noise rate from the training set appears to be a natural signal to be used in the definition of this curriculum, previous approaches generally rely on arbitrary thresholds or pre-defined selection functions to the best of our knowledge. This paper addresses this research gap by proposing a new noisy-label learning graphical model that can easily accommodate state-of-the-art (SOTA) noisy-label learning methods and provide them with a reliable noise rate estimate to be used in a new sample selection curriculum. We show empirically that our model integrated with many SOTA methods can improve their results in many IDN benchmarks, including synthetic and real-world datasets.
Extra material
Cite:
@article{garg2023noisy,
title={Noisy-label Learning with Sample Selection based on Noise Rate Estimate},
author={Garg, Arpit and Nguyen, Cuong and Felix, Rafael and Do, Thanh-Toan and Carneiro, Gustavo},
journal={arXiv preprint arXiv:2305.19486},
year={2023}
}
Rafa Felix
PhD, that climbs and enjoy long distance rides.