Applications of Generative Adversarial Networks in Single Image Datasets

Document Type

Article

Authors

Dylan E. Cramer, University of Minnesota, MorrisFollow

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