Methods for finetuning generative models for concept-driven personalization generally
achieve strong results for subject-driven or style-driven generation. Recently, low-rank …

Automatic high-quality rendering of anime scenes from complex real-world images is of
significant practical value. The challenges of this task lie in the complexity of the scenes, the …

The adaptation of a Generative Adversarial Network (GAN) aims to transfer a pre-trained
GAN to a target domain with limited training data. In this paper, we focus on the one-shot …

Deep generative models, which target reproducing the given data distribution to produce
novel samples, have made unprecedented advancements in recent years. Their technical …

One-shot generative domain adaption aims to transfer a pre-trained generator on one
domain to a new domain using one reference image only. However, it remains very …

With the remarkable advent of text-to-image diffusion models image editing methods have
become more diverse and continue to evolve. A promising recent approach in this realm is …

This work aims to transfer a Generative Adversarial Network (GAN) pre-trained on one
image domain to another domain referred to as few as just one reference image. The …

In machine learning, generative modeling aims to learn to generate new data statistically
similar to the training data distribution. In this paper, we survey learning generative models …

In this paper, we address the challenge of compressing generative adversarial networks
(GANs) for deployment in resource-constrained environments by proposing two novel …

Abstract This paper introduces Unified Language-driven Zero-shot Domain Adaptation
(ULDA) a novel task setting that enables a single model to adapt to diverse target domains …