GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Generative Adversarial Networks (GANs) have achieved remarkable achievements in image
synthesis. These successes of GANs rely on large scale datasets, requiring too much cost …

Learning generative image models from various domains efficiently needs transferring
knowledge from an image synthesis model trained on a large dataset. We present a recipe …

Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
Widespread interest exists in incorporating DMs into downstream applications, such as …

Modern GANs excel at generating high-quality and diverse images. However, when
transferring the pretrained GANs on small target data (eg, 10-shot), the generator tends to …

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

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 …

Abstract Data-Efficient GANs (DE-GANs), which aim to learn generative models with a
limited amount of training data, encounter several challenges for generating high-quality …

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