Deep generative models, which target reproducing the data distribution to produce novel
images, have made unprecedented advancements in recent years. However, one critical …

Image composition is an important operation in image processing, but the inconsistency
between foreground and background significantly degrades the quality of composite image …

Existing few-shot image generation approaches typically employ fusion-based strategies,
either on the image or the feature level, to produce new images. However, previous …

Given only a few available images for a novel unseen category, few-shot image generation
aims to generate more data for this category. Previous works attempt to globally fuse these …

In order to generate images for a given category, existing deep generative models generally
rely on abundant training images. However, extensive data acquisition is expensive and fast …

Given a composite image with inharmonious foreground and background, image
harmonization aims to adjust the foreground to make it compatible with the background …

Image generation relies on massive training data that can hardly produce diverse images of
an unseen category according to a few examples. In this paper, we address this dilemma by …

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 …

Abstract Background: Generative adversarial networks (GANs) are vital techniques for
synthesizing high-fidelity images. Recent studies have applied them to generation tasks …

Few-shot image generation is a challenging task even using the state-of-the-art Generative
Adversarial Networks (GANs). Due to the unstable GAN training process and the limited …