Abstract Training Generative Adversarial Networks (GANs) on high-fidelity images usually
requires a vast number of training images. Recent research on GAN tickets reveals that …

Despite excellent performance in image generation, Generative Adversarial Networks
(GANs) are notorious for its requirements of enormous storage and intensive computation …

Recent years have witnessed a significant interest in the “generative adversarial
networks”(GANs) due to their ability to generate high-fidelity data. Many models of GANs …

In this article, we design the first full software/hardware stack, called Uni-OPU, for an efficient
uniform hardware acceleration of different types of transposed convolutional (TCONV) …

The rapid progress in artificial intelligence-generated content (AIGC), especially with
diffusion models, has significantly advanced development of high-quality video generation …

Face identity editing (FIE) shows great value in AI content creation. Low-resolution FIE
approaches have achieved tremendous progress, but high-quality FIE struggles. Two major …

Since its invention, generative adversarial networks (GANs) have shown outstanding results
in many applications. GANs are powerful, yet resource-hungry deep learning models. The …

Abstract Generative Adversarial Networks (GANs) have proven to exhibit remarkable
performance and are widely used across many generative computer vision applications …

Quantum machine learning uses quantum mechanical concepts of superposition of states to
make the decision. In this work, we used these quantum advantages to enhance deep …

One highly promising direction for enabling flexible real-time on-device image editing is
utilizing data distillation by leveraging large-scale text-to-image diffusion models to generate …