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.
| Item Type: |
Article
|
| Keywords: |
WGAN; Improving; Imbalanced; Classification; Performance; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
15250 |
| Depositing User: |
Rozenn Dahyot
|
| Date Deposited: |
17 Jan 2022 12:48 |
| Journal or Publication Title: |
CEUR Workshop Proceedings |
| Publisher: |
CEUR |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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