Generative Adversarial Networks are all about generating new content. GANs are capable of learning some distribution and creating a new sample from that distribution. That sample might just be a new point on a line that isn't present in our training data, but it could also be a new point in a very complex dataset. GANs have been used to generate new music, sounds, and images. According to Yann LeCun, adversarial training is the coolest thing since sliced bread (https://www.quora.com/session/Yann-LeCun/1). I'm not sure that sliced bread is especially cool, but Yann LeCun is a very cool guy so I'll take his word for it. Regardless, GANs are incredibly popular and while perhaps not as practical as some of the other topics we've covered in a business setting yet, they deserve some consideration in our survey of deep learning techniques.
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