Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Talking human face generation aims at synthesizing a natural human face that talks in
correspondence to the given text or audio series. Implementing the recently developed …

Abstract Graph-based Collaborative Filtering (GCF) is widely used in personalized
recommendation systems. However, GCF suffers from a fundamental problem where …

In this paper, we propose a novel normalization method called gradient normalization (GN)
to tackle the training instability of Generative Adversarial Networks (GANs) caused by the …

Abstract Generative Adversarial Networks (GANs) are powerful tools for image synthesis.
However, they require access to vast amounts of training data, which is often costly and …

Abstract Defects in Laser Powder Bed Fusion (L-PBF) parts often result from the meso-scale
dynamics of the molten alloy near the laser, known as the melt pool. Experimental in-situ …

Abstract Underwater Image Rendering aims to generate a true-to-life underwater image from
a given clean one, which could be applied to various practical applications such as …

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-
resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) …

Abstract Generative Adversarial Networks (GANs) significantly advanced image generation
but their performance heavily depends on abundant training data. In scenarios with limited …

The purpose of image inpainting is to recover scratches and damaged areas using context
information from remaining parts. In recent years, with the development of convolutional …