Recently, conditional diffusion models have gained popularity in numerous applications due
to their exceptional generation ability. However, many existing methods are training …

Various stuff and things in visual data possess specific traits, which can be learned by deep
neural networks and are implicitly represented as the visual prior, eg, object location and …

Abstract Training Generative Adversarial Networks (GANs) remains a challenging problem.
The discriminator trains the generator by learning the distribution of real/generated data …

Substantial training data is necessary to train an effective generative adversarial network
(GANs), without which the discriminator is easily overfitting, causing the sub-optimal models …

Abstract Generative Adversarial Networks (GANs) have received considerable attention due
to its outstanding ability to generate images. However, training a GAN is hard since the …

The field of image generation models has seen substantial progress, characterized by a
proliferation of diverse generative models and their associated outputs. However, there …

Cross-scene classification of hyperspectral image (HSI) based on single-source domain
generalization (SDG) focuses on developing a model that can effectively classify images …

Recently, many studies have highlighted that training Generative Adversarial Networks
(GANs) with limited data suffers from the overfitting of the discriminator (D). Existing studies …

Audio-to-talking face generation stands at the forefront of advancements in generative AI. It
bridges the gap between audio and visual representations by generating synchronized and …

Generative adversarial networks (GANs) have shown notable accomplishments in remote
sensing (RS) domain. However, this article reveals that their performance on RS images …