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Image generation allows the creation of visual content in a convenient manner. It is critical for enhancing digital experiences, from video games to virtual reality, enabling more engaging and immersive experiences. In current technologies, Generative Adversarial Networks (GANs) have achieved significant success but face challenges like training instability and mode collapse. By utilizing the Wasserstein distance, Wasserstein GAN (WGAN) enhances conventional GANs; however, its weight clipping method may not be ideal. In this study, WGAN with gradient penalty (WGAN-GP) and WGAN with weight clipping (WGAN-CP) are compared, which aims to enhance stability by better enforcing the Lipschitz constraint. For comparison, these approaches are validated using Fashion Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR)-10 datasets. Experimental results show WGAN-GP produces higher quality images and more stable training than WGAN-CP. However, WGAN-GP also requires longer training times and computational burden. The findings highlight a trade-off between training efficiency and output quality, guiding the choice of technique based on specific application needs.
Jiajun Gao (Fri,) studied this question.