The Workings Behind Google Deep Dream Generator

The Deep Dream Generator is another of Google’s marvelous creations.  Originally developed for the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in 2014, it was later released into the market in 2015.  This computer vision program can find and enhance patterns in images to create dreamlike pictures.  The way in which the platform works is through the use of convolutional neural networks.  This is essentially a term for describing a forward-fed neural network that involves neuron connectivity patterns responding to the overlapping regions in the visual field.

Deep Dream uses the Python coding language in Caffe, which is a deep learning framework that can express architecture and code quickly.  The great thing about this platform is that it transforms your everyday photographs into individual works or art, and you get to watch it happen right in front of your very eyes.  Once Google had published their software techniques and made their code open source, various apps and desktop software appeared that enabled users to transform their photos in the blink of an eye.

It is all made possible through the extensive research that has been done by neural networks.  It is the various levels of neural networks that can process an image that has been input into the program.  These artificial neurons are processed through three layered networks: low, intermediate, and high-level layers.  While the lower levels are responsible for basic textures, corners, and edges, the higher levels are responsible for, the more detailed input images like buildings and objects that are very elaborate in appearance.

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