Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

Underwater images suffer from severe distortion, which degrades the accuracy of object
detection performed in an underwater environment. Existing underwater image …

The recent explosive interest on transformers has suggested their potential to become
powerful``universal" models for computer vision tasks, such as classification, detection, and …

Training generative adversarial networks (GAN) using too little data typically leads to
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …

Score-based generative models (SGMs) have demonstrated remarkable synthesis quality.
SGMs rely on a diffusion process that gradually perturbs the data towards a tractable …