Inceptionism: Going Deeper into Neural Networks by Alexander Mordvintsev, Christopher Olah, and Mike Tyka.
From the post:
Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. So let’s take a look at some simple techniques for peeking inside these networks.
We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want. The network typically consists of 10-30 stacked layers of artificial neurons. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network’s “answer” comes from this final output layer.
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.
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Have you ever looked under the hood of a neural network? If not, you are in for a real treat! As a bonus, this research may help you understand why some models work and others don’t.
Same title but images as seen by neural networks before it reaches an outcome.
I don’t think anyone has captured an interruption of image processing in the human brain. With a neural network, that is a reality.
Enjoy!