Which tool is more suitable for visualizing the distribution of multiple real and synthetic image datasets, t-SNE or PCA? I am doing a thesis on the generation of synthetic data for training a deep learning model and evaluating it on real data. I have a few different real datasets, and I generated multiple synthetic datasets with different parameters. I want to write a chapter with data description and would like to be able to visualize them. But I am wondering which tool would make more sense, t-SNE or PCA. What do you think?

Assuming you are referring to obtaining a visual representation of samples' variability/similarity, I think it really depends on the scope, but I'd probably favor t-SNE over PCA given that the data is perceptual and thus likely to contain non-linear patterns.
Have you considered plotting embeddings of your images instead of the raw data? If you are applying auto-encoders or VAEs the latent representation could be a nice input for visualization. Also, if you go down the t-SNE path, you might want to check UMAP as well (this nice post could be a good starting point). Good luck with your thesis!
