Year: 2024
Type: article
Abstract: State-of-the-art GANs such as StyleGAN2 have been regarded as being capable of mimicking a real data distribution and are able to produce photorealistic images when trained using large datasets of images such as faces of people, animals or natural scenes.However, in the case of images that are specific to a particular engineering domain like radiology or material science, StyleGAN2-generated images, though still seemingly realistic to the untrained eye, have been shown not to follow known distributions of key statistics specific to the domain.This non-adherence of important statistical distributions renders the generated images unsuitable for downstream tasks in that domain.
Author: Kamath, Nidhish Ganesh
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220758142