# A Comparative Study of GAN-Generated Handwriting Images and MNIST Images using t-SNE Visualization

arXiv: https://arxiv.org/abs/2305.09786 · HTML: https://mlsanity.com/paper/2305.09786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 4 of 4)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Data augmentation approaches for improving animal audio classification](https://mlsanity.com/paper/1912.07756/) (2019)

## Abstract (arXiv)

The quality of GAN-generated images on the MNIST dataset was explored in this paper by comparing them to the original images using t-distributed stochastic neighbor embedding (t- SNE) visualization. A GAN was trained with the dataset to generate images and the result of generating all synthetic images, the corresponding labels were saved. The dimensionality of the generated images and the original MNIST dataset was reduced using t-SNE and the resulting embeddings were plotted. The rate of the GAN-generated images was examined by comparing the t-SNE plots of the generated images and the original MNIST images. It was found that the GAN- generated images were similar to the original images but had some differences in the distribution of the features. It is believed that this study provides a useful evaluation method for assessing the quality of GAN-generated images and can help to improve their generation in the future.