Mordvintsev’s process was more Fast and Furious. Previously, the mission of convolutional neural nets was to proceed in a defensive-driving fashion, straining to filter out wrong turns and make accurate predictions. In the middle of the network’s usual practice of trying to verify a nascent sense that a particular pattern may be a target object, he told the network to skip directly to “Go,” and then start making the object. In other words he would flip the function of the neural net from recognizing what was there to generating stuff that might not be there. Just like, whatever it sees in this batch of images, let’s have more of it. Let’s find something that increases the magnitude of the activation vector, he told himself. On this restless night in May, while his wife and child slept, he did the coding equivalent of fiddling the dials to change the objective of the neural net.
#XDREAM NEURAL NETWORK CODE#
Mordvintsev wanted to continue down that path, with a wicked turn: He was writing code to make a neural net create meaningful images that weren’t there at all, at least not as humans could tell - visions born of machines, oozing out of the metaphorically neural connections in the system. By looking at those images, the researchers had a better idea of what the neural network was up to at that instant. One team in particular, from the Visual Geometry Group at the University of Oxford, had taken an interesting approach to analyzing how successful vision systems can recognize (classify) objects: at a certain point in the training process, they got the networks to generate images of what they were perceiving. ConvNets are a specialized form generally used for vision recognition they take the biological metaphor farther by not only using a neuron-style learning system, but by employing the neurons in a similar fashion to the way light receptors are arranged in the visual cortex. His curiosity was piqued by one the abiding mysteries of neural nets and deep learning: why did they work so well and what the hell went on inside them? Others had been asking the same question, using what are known as convolutional neural nets (ConvNets) to probe vision recognition systems at various points in the process. As an NN newbie, Mordvintsev was teaching himself about the field, absorbing key papers and playing with systems already trained to recognize certain objects.
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