Inceptionism: Going Deeper into Neural Networks
One of the challenges of neural networks is understanding what exactly goes on at each layer. We know that after training, each layer progressively extracts higher and higher-level features of the image, until the final layer essentially makes a decision on what the image shows. For example, the first layer maybe looks for edges or corners. Intermediate layers interpret the basic features to look for overall shapes or components, like a door or a leaf. The final few layers assemble those into complete interpretations–these neurons activate in response to very complex things such as entire buildings or trees. One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in %E2%80%9CBanana.%E2%80%9D Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana
http://googleresearch.blogspot.be/2015/06/inceptionism-going-deeper-into-neural.html