Binary cross-entropy with dynamical clipping
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA2302717" target="_blank" >RIV/61988987:17610/22:A2302717 - isvavai.cz</a>
Výsledek na webu
<a href="https://link.springer.com/article/10.1007/s00521-022-07091-x" target="_blank" >https://link.springer.com/article/10.1007/s00521-022-07091-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-022-07091-x" target="_blank" >10.1007/s00521-022-07091-x</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Binary cross-entropy with dynamical clipping
Popis výsledku v původním jazyce
We investigate the adverse effect of noisy labels in a training dataset on a neural network's precision in an image classification task. The importance of this research lies in the fact that most datasets include noisy labels. To reduce the impact of noisy labels, we propose to extend the binary cross-entropy by dynamical clipping, which clips all samples' loss values in a mini-batch by a clipping constant. Such a constant is dynamically determined for every single mini-batch using its statistics. The advantage is the dynamic adaptation to any number of noisy labels in a training dataset. Thanks to that, the proposed binary cross-entropy with dynamical clipping can be used in any model utilizing cross-entropy or focal loss, including pre-trained models. We prove that the proposed loss function is an alpha-calibrated classification loss, implying consistency and robustness to noise misclassification in more general asymmetric problems. We demonstrate our loss function's usefulness on Fashion MNIST, CIFAR-10, CIFAR-100 datasets, where we heuristically create training data with noisy labels and achieve a nice performance boost compared to the standard binary cross-entropy. These results are also confirmed in the second experiment, where we use a trained model on Google Images to classify the ImageWoof dataset, and the third experiment, where we deal with the WebVision and ANIMAL-10N datasets. We also show that the proposed technique yields significantly better performance than the gradient clipping. Code: gitlab.com/irafmai/clipping_cross_entropy
Název v anglickém jazyce
Binary cross-entropy with dynamical clipping
Popis výsledku anglicky
We investigate the adverse effect of noisy labels in a training dataset on a neural network's precision in an image classification task. The importance of this research lies in the fact that most datasets include noisy labels. To reduce the impact of noisy labels, we propose to extend the binary cross-entropy by dynamical clipping, which clips all samples' loss values in a mini-batch by a clipping constant. Such a constant is dynamically determined for every single mini-batch using its statistics. The advantage is the dynamic adaptation to any number of noisy labels in a training dataset. Thanks to that, the proposed binary cross-entropy with dynamical clipping can be used in any model utilizing cross-entropy or focal loss, including pre-trained models. We prove that the proposed loss function is an alpha-calibrated classification loss, implying consistency and robustness to noise misclassification in more general asymmetric problems. We demonstrate our loss function's usefulness on Fashion MNIST, CIFAR-10, CIFAR-100 datasets, where we heuristically create training data with noisy labels and achieve a nice performance boost compared to the standard binary cross-entropy. These results are also confirmed in the second experiment, where we use a trained model on Google Images to classify the ImageWoof dataset, and the third experiment, where we deal with the WebVision and ANIMAL-10N datasets. We also show that the proposed technique yields significantly better performance than the gradient clipping. Code: gitlab.com/irafmai/clipping_cross_entropy
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10200 - Computer and information sciences
Návaznosti výsledku
Projekt
<a href="/cs/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centrum pro výzkum a vývoj metod umělé intelligence v automobilovém průmyslu regionu</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
NEURAL COMPUT APPL
ISSN
0941-0643
e-ISSN
1433-3058
Svazek periodika
—
Číslo periodika v rámci svazku
14
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
13
Strana od-do
12029-12041
Kód UT WoS článku
000780838900001
EID výsledku v databázi Scopus
2-s2.0-85128058703