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BAGAN: Data Augmentation with Balancing GAN
[article]
2018
arXiv
pre-print
Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial training all available images of majority and minority classes. The generative model learns useful
arXiv:1803.09655v2
fatcat:qbramjshfvbz3ap5qwa6nmhc6a