To prepare to train their neural network, the team first generated 55,116 masks of random streaks and holes of arbitrary shapes and sizes for training. They also generated nearly 25,000 for testing. These were further categorized into six categories based on sizes relative to the input image, in order to improve reconstruction accuracy.
Source: nvidia.com
As detailed in a paper published to arXiv, the neural net is actually a generative adversarial network (GAN), which is a way of training a machine learning algorithm without human supervision. In GANs, two neural nets are pitted against one another: One neural net generates new images and tries to trick the other neural net into thinking the images are real. If the other neural net is able to tell the generated images are false
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Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.
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