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 …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

Abstract Language models have been successfully used to model natural signals, such as
images, speech, and music. A key component of these models is a high quality neural …

We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that
efficiently samples large-scale foundational image diffusion models in just 1-4 steps while …

Despite the tantalizing success in a broad of vision tasks, transformers have not yet
demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In …

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense
covers largely prohibits its wider applications. While several recent works have attempted to …

The output of text-to-image synthesis systems should be coherent, clear, photo-realistic
scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal …

We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …