High-level features of a neural network I understand how to build and train a neural network like shown below, as well as those low-level features/filters. I wonder what are those high-level features: how exactly do you obtain them from a trained neural network? (Are those like the "eigenfaces"?)



Note: the image is by NVIDIA, and I don't know the specifics of the classification problem here. If needed, suppose the network is trained to distinguish human from cat.
Its about how the neural net learn inside. Usually in deep neural network you have multiple layers, the first layers will learn the low level feature then the more you approach the output layer the more the layers will learn the high level feature. 
Here its identifying faces task, that's why you see that in the first layers small patterns are learned (mainly edges ...) then faces components ...

How to obtain them : you should watch what makes neuron activated in each layer depending on the input. As you know each neuron will be activated (once the DNN is trained) for specific input combinations. 
By visualizing that you can get an idea about what exactly each layer has learned in term of high-low level features.