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In my previous post about generative adversarial networks, I went over a simple method to training a network that could generate realistic-looking images. However, there were a couple of downsides to using a plain GAN. First, the images are generated off some arbitrary noise. If you wanted to generate a picture with specific features, there's no way of determining which initial noise values would
There's been a lot of advances in image classification, mostly thanks to the convolutional neural network. It turns out, these same networks can be turned around and applied to image generation as well. If we've got a bunch of images, how can we generate more like them? A recent method, Generative Adversarial Networks, attempts to train an image generator by simultaneously training a discriminator
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