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	Author	Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva
	Title	Class-conditional Importance Weighting for Deep Learning with Noisy Labels			Type	Conference Article
	Year	2022	Publication	17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications	Abbreviated Journal
	Volume	5	Issue		Pages	679-686
	Keywords	Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels
	Abstract	Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results.
	Address	Virtual; February 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	VISAPP
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ NMM2022			Serial	3798
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