Deep generative models, which target reproducing the data distribution to produce novel
images, have made unprecedented advancements in recent years. However, one critical …

Abstract Compressed Image Super-resolution has achieved great attention in recent years,
where images are degraded with compression artifacts and low-resolution artifacts. Since …

Abstract Generative Adversarial Networks (GAN) show excellent performance in various
problems of computer vision, computer graphics, and machine learning, but require large …

Evaluation metrics in image synthesis play a key role to measure performances of
generative models. However, most metrics mainly focus on image fidelity. Existing diversity …

Recent methods for conditional image generation benefit from dense supervision such as
segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ …

Various models based on StyleGAN have gained significant traction in the field of image
synthesis, attributed to their robust training stability and superior performances. Within the …

As one of the representative models in the field of image generation, generative adversarial
networks (GANs) face a significant challenge: how to make the best trade-off between the …

This article presents a new perspective from control theory to interpret and solve the
instability and mode collapse problems of generative adversarial networks (GANs). The …

The existing deepfake detection methods have reached a bottleneck in generalizing to
unseen forgeries and manipulation approaches. Based on the observation that the deepfake …

Generative adversarial networks heavily rely on large datasets and carefully chosen model
parameters to avoid model overfitting or mode collapse. Cutout with patch-loss …