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001-es BibID:	BIBFORM115594
035-os BibID:	(WoS)001143123700003 (Scopus)85174848737
Első szerző:	Fazekas István (matematikus, informatikus)
Cím:	Deep Learning from Noisy Labels with Some Adjustments of a Recent Method / István Fazekas, László Fórián, Attila Barta
Dátum:	2023
ISSN:	2061-2079
Megjegyzések:	In this paper we have used JoCoR, a fairly recent method for learning with label noise, that makes use of two neural networks with a joint loss function using an additional contrastive loss to increase the agreement between them. This method can be extended to more than two networks in a straightforward way. We have carried out experiments on the CIFAR-10 and CIFAR-100 datasets (contaminated by synthetic label noise) with this kind of extension using several contrastive losses. We have concluded that it makes a significant improvement if we use a third network, especially when we use Kullback-Leibler terms for all possible pairs of softmax outputs. Further extension also means some kind of improvement, but in the case of the CIFAR datasets, those were not so significant, maybe except the cases with lower ratio of label noise.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk Deep Learning Noisy Labels Classification Neural Networks Supervised Learning
Megjelenés:	Infocommunications Journal. - 15 : Special Issue (2023), p. 9-12. -
További szerzők:	Fórián László Barta Attila (1989-) (alkalmazott matematikus)
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00002 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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