Feng, Chen;
Tzimiropoulos, Georgios;
Patras, Ioannis;
(2024)
NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels.
IEEE Transactions on Circuits and Systems for Video Technology
10.1109/tcsvt.2024.3426994.
(In press).
Preview |
PDF
NoiseBox_Towards_More_Efficient_and_Effective_Learning_with_Noisy_Labels.pdf - Accepted Version Download (6MB) | Preview |
Abstract
Despite the large progress in supervised learning with neural networks, there are significant challenges in obtaining high-quality, large-scale and accurately labelled datasets. In such contexts, how to learn in the presence of noisy labels has received more and more attention. Addressing this relatively intricate problem to attain competitive results predominantly involves designing mechanisms that select samples that are expected to have reliable annotations. However, these methods typically involve multiple off-the-shelf techniques, resulting in intricate structures. Furthermore, they frequently make implicit or explicit assumptions about the noise modes/ratios within the dataset. Such assumptions can compromise model robustness and limit its performance under varying noise conditions. Unlike these methods, in this work, we propose an efficient and effective framework with minimal hyperparameters that achieves SOTA results in various benchmarks. Specifically, we design an efficient and concise training framework consisting of a subset expansion module responsible for exploring non-selected samples and a model training module to further reduce the impact of noise, called NoiseBox . Moreover, diverging from common sample selection methods based on the "small loss" mechanism, we introduce a novel sample selection method based on the neighbouring relationships and label consistency in the feature space. Without bells and whistles, such as model co-training, self-supervised pre-training and semi-supervised learning, and with robustness concerning the settings of its few hyper-parameters, our method significantly surpasses previous methods on both CIFAR10/CIFAR100 with synthetic noise and real-world noisy datasets such as Red Mini-ImageNet, WebVision, Clothing1M and ANIMAL-10N.
| Type: | Article |
|---|---|
| Title: | NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2024.3426994 |
| Publisher version: | http://dx.doi.org/10.1109/tcsvt.2024.3426994 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Noise, Noise measurement, Training, Computational modeling, Predictive models, Supervised learning, Entropy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194620 |
Archive Staff Only
 |
View Item |
