It is a method called “Activation Maximization” introduced in a paper by Bengio et al. The goal is to pass a random image through the network until a certain activation layer (previously detected as activating higher than others for a particular image). By differentiating the pixels of the random image in the direction of the highest ascent (maximum mean activation), the results should be an image that activates the most for that particular feature map.
I can attest that the image is changing properly and that the mean activation is being increased from .98 to 3.75 where it plateaus. Maybe the problem is with my post-processing of the resulting image, since I’m not an expert in image manipulation.