CNN to learn and visualize 2d features In the paper Large-Margin Softmax Loss for Convolutional Neural Networks the author has a figure as below:


He is claiming that he is using only 2d features to classify MNIST with a CNN. How is he doing that? Is he reducing the dimension of MNIST images to 2 and how? How do you visualize such figures in 2D if the classification is for 10 classes?
I think I got my answer from the following post:
http://unixnme.blogspot.com/2018/04/dimensionality-reduction-and-scattered.html