Peeking Inside Convolutional Neural Networks

What is interesting to notice, is that the network doesn't seem to have learned detailed representations of faces. In e.g. the visualization featuring the collar, the face looks more like a spooky flesh-colored blob than a face. This might be an artifact of the visualization process, but it's not entirely unlikely that the network have either not found it necessary to learn the details, or not had the capacity to learn them. There also are a surprisingly large number of units that detect dog-related features. I counted somewhere around 50, out of 512 units in the layer in total, which means a surprising 10% of the network may be dedicated solely to dogs.