Visualizing inputs that maximally activate feature maps of a convnet
Being able to visualize input stimuli that excite individual feature maps in a convnet is a great way to learn about it’s internal workings, and can also come in handy while debugging networks. Matthew Zeiler and Rob Fergus demonstrated in 2013 that the feature maps are activated by progressively complex features as we move deeper into the network. They visualized these input features by mapping feature map activities back to the input pixel space by using a deconvnet. Another way to visualize these features is by performing gradient descent in the input space, which I first read about in this post by Francois Chollet, and then in A Neural Algorithm of Artistic Style by Gatys et al. ...