Computer Vision and Computer Hallucinations » American Scientist

Computer Vision
&…Hallucinations,y.2015,no.5,content.true,page.1,css.print/issue.aspx Instead of training a neural network, train an image to fit it. Dreams emerge
“The algorithm behind the deep dream images was devised by Alexander Mordvintsev, a Google software engineer in Zurich. In the blog posts he was joined by two coauthors: Mike Tyka, a biochemist, artist, and Google software engineer in Seattle; and Christopher Olah of Toronto, a software engineering intern at Google.

Here’s a recipe for deep dreaming. Start by choosing a source image and a target layer within the neural network. Present the image to the network’s input layer, and allow the recognition process to proceed normally until it reaches the target layer. Then, starting at the target layer, apply the backpropagation algorithm that corrects errors during the training process. However, instead of adjusting connection weights to improve the accuracy of the network’s response, adjust the source image to increase the amplitude of the response in the target layer. This forward-backward cycle is then repeated a number of times, and at intervals the image is resampled to increase the number of pixels.”

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