Document Type
Article
Abstract
One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.
Recommended Citation
Cramer, Dylan E.
(2023)
"Applications of Generative Adversarial Networks in Single Image Datasets,"
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 10:
Iss.
1, Article 2.
DOI: https://doi.org/10.61366/2576-2176.1118
Available at:
https://digitalcommons.morris.umn.edu/horizons/vol10/iss1/2
Primo Type
Article