In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …

Discriminator plays a vital role in training generative adversarial networks (GANs) via
distinguishing real and synthesized samples. While the real data distribution remains the …

Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for
training a prediction model. Accordingly, generative models [eg, generative adversarial …

Generative adversarial networks (GANs) are a powerful generative technique but frequently
face challenges with training stability. Network architecture plays a significant role in …

In this paper, we introduce Auto-GAS, the first training-free Generative Architecture Search
(GAS) framework enabled by an auto-discovered proxy. Generative models like Generative …

Generative adversarial networks (GANs) have proven successful in image generation tasks.
However, GAN training is inherently unstable. Although many works try to stabilize it by …

Deep learning, a subset of machine learning, aims to imbue machines with human-like
perception, learning, and intelligence through advanced technology. It has made significant …

Recently, a number of Neural Architecture Search (NAS) methods have been proposed to
automate the design of Generative Adversarial Networks (GANs). However, due to the …

Abstract Generative Adversarial Networks (GANs) have emerged as a key technology in
artificial intelligence, especially in image generation. However, traditionally hand-designed …

Abstract Generative Adversarial Networks (GANs) have brought surprises in image
generation and processing, particularly excelling in alleviating the problem of sparse …