| Title | OA-GAN: Overfitting Avoidance Method of GAN Oversampling Based on xAI |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kim, Jiha, Park, Hyunhee |
| Conference Name | 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) |
| Date Published | Aug. 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6476-2 |
| Keywords | Deep Learning, Discriminator, gan, generative adversarial networks, generator, Generators, Overfitting, pubcrawl, resilience, Resiliency, Scalability, simulation, Training, xai |
| Abstract | The most representative method of deep learning is data-driven learning. These methods are often data-dependent, and lack of data leads to poor learning. There is a GAN method that creates a likely image as a way to solve a problem that lacks data. The GAN determines that the discriminator is fake/real with respect to the image created so that the generator learns. However, overfitting problems when the discriminator becomes overly dependent on the learning data. In this paper, we explain overfitting problem when the discriminator decides to fake/real using xAI. Depending on the area of the described image, it is possible to limit the learning of the discriminator to avoid overfitting. By doing so, the generator can produce similar but more diverse images. |
| URL | https://ieeexplore.ieee.org/document/9528594 |
| DOI | 10.1109/ICUFN49451.2021.9528594 |
| Citation Key | kim_oa-gan_2021 |
OA-GAN: Overfitting Avoidance Method of GAN Oversampling Based on xAI