GAN-based Image Generation Techniques Exploiting Latent Vector Distribution and Edge Loss Methods on Limited Datasets

Abstract

Generative Adversarial Networks (GANs) have demonstrated remarkable capabilities in image generation, surpassing the performance of previous image generation models. However, GANs require large training datasets to facilitate proper learning. GANs have inherent problems such as the mode collapse problem, where identical images are generated, and instability problem, where the generator and the discriminator fail to form a successful adversarial relationship. These problems are particularly common when the availability of training data is limited. In this paper, we propose three techniques to address these challenges. Firstly, Common Feature Training (CFT) is introduced to enhance performance by training the Generator to recognize common features, thereby mitigating instability problems. Secondly, Mean Rescaling (MR) is employed to mitigate the mode collapse problem arising from sampling latent vectors with identical means and variances. Thirdly, an edge loss method is implemented, where the edge difference values between real and generated images are added to the GANs loss. This contributes to the classification of shapes, thereby mitigating the mode collapse problem and instability problem. Comparative experimental results illustrate improvements in the highlighted issues, and the performance enhancement is validated by metrics, namely Fréchet Inception Distance (FID) and Inception Score (IS). © 2023 Copyright for this paper by its authors.