Every 100 frames (4 seconds) the next layer is targeted until the lowest layer is reached.
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Check out this video that uses the code iteratively. Each frame is recursively fed back to the network starting with a frame of random noise. The code can be applied on both static images and videos. Google’s Research team made their visualization code public after gaining a great amount of interest from programmers and artists alike. Here’s a video of someone using MIT’s Places CNN. So it’s not really limited to dogs but rather to data sets fed to the code. The data is stored in high abstraction which results to the creation of hybrid animals. Images from Research at Google Why are there a lot of animals?Īccording to Google’s Research team, this particular algorithm was trained with a large number of animal images and naturally, it is likely to interpret shapes as animals. The Google Research team found out that feeding the algorithm iteratively with its own output yielded interesting results.Ĭheck out some of the psychedelic images generated by Google’s Deep Dream below:įrom Lincoln Harrison’s Startrail GalleryĪ Sunday Afternoon on the Island of La Grande Jatte The final few layers assemble those into complete interpretations-these neurons activate in response to very complex things such as entire buildings or trees. Intermediate layers interpret the basic features to look for overall shapes or components, like a door or a leaf. “For example, the first layer maybe looks for edges or corners. The research team explained in their post “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.” Such algorithm But what do these machine see? A psychedelic trip apparently. By feeding it dog images, larger quantity for higher accuracy, it could be trained to spot dogs in images, tell if there aren’t any, or say it is unsure.
Say you want train a machine how a dog looks like. In the past, classifying images into categories was nearly impossible but advances in cognitive science made it possible for machines to distinguish images with a high degree of accuracy. Derived from biological central nervous systems, they are used in image classification and speech recognition by feeding them large amount of input data to “train” them.
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Two weeks ago, Google’s research team featured in their blog a visualization tool designed to understand how neural networks work and how to replicate them artificially.Īrtificial neural networks are learning models essential in machine learning. Throw in a dog that looks like a slug and you get Google’s artificial neural network “Deep Dream”. Picture yourself on a boat in a river with tangerine trees and marmalade skies.