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The quality of GAN-generated images on the MNIST dataset was explored in thispaper by comparing them to the original images using t-distributed stochasticneighbor embedding (t- SNE) visualization. A GAN was trained with the datasetto generate images and the result of generating all synthetic images, thecorresponding labels were saved. The dimensionality of the generated images andthe original MNIST dataset was reduced using t-SNE and the resulting embeddingswere plotted. The rate of the GAN-generated images was examined by comparingthe t-SNE plots of the generated images and the original MNIST images. It wasfound that the GAN- generated images were similar to the original images buthad some differences in the distribution of the features. It is believed thatthis study provides a useful evaluation method for assessing the quality ofGAN-generated images and can help to improve their generation in the future.

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