  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.
