Bhatia, Snehal and Dahyot, Rozenn
(2019)
Using WGAN for Improving Imbalanced Classification Performance.
CEUR Workshop Proceedings, 2563.
pp. 365-375.
ISSN 1613-0073
Abstract
This paper investigates data synthesis with a Generative Adversarial Network (GAN) for augmenting the amount of data used for
training classifiers (in supervised learning) to compensate for class imbalance (when the classes are not represented equally by the same number of
training samples). Our data synthesis approach with GAN is compared
with data augmentation in the context of image classification. Our experimental results show encouraging results in comparison to standard
data augmentation schemes based on image transforms.
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