It's interesting how amenable image classification neural networks are to the "take working model, peel off last layer or two, retrain for a new application" approach. I've seen this suggested as working pretty well in a few instances.

I guess the interpretation is that the first few normalize->convolution->pool->dropout layers are basically achieving something broadly analogous to the initial feature extraction steps that used to be the mainstay in this area (PCA/ICA, HOG, SIFT/SURF, etc.), and are reasonably problem-independent.