Abstract Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …

Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the
paradigm of natural language comprehension and generation. Despite their impressive …

Generative adversarial networks (GANs) have demonstrated impressive image generation
quality and semantic editing capability of real images, eg, changing object classes …

Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to
render desired attributes, which has received significant attention due to its broad practical …

Face age synthesis is the determination of how a person looks in the future or the past by
reconstructing their facial image. Determining the change in the human face over the years …

The StyleGAN family succeed in high-fidelity image generation and allow for flexible and
plausible editing of generated images by manipulating the semantic-rich latent style space …

Generative Adversarial Networks (GANs) are susceptible to bias, learned from either the
unbalanced data, or through mode collapse. The networks focus on the core of the data …

Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs)
has attracted a great deal of attention and effort from researchers in recent years. Due to the …

Static networks, known for their fixed architectures and weights, have been widely employed
in diverse applications. However, the emergence of dynamic neural networks (DNNs) …