Photorealistic Image Colourization with Generative Adversarial Nets

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Photorealistic Image Colourization with Generative Adversarial Nets Mathew Hall, Matthew Walker, Meghan Lele

Project Goal Produce realistic colour image from given B & W image final product black and white image realistic colour image Produce realistic colour image from given B & W image

Neural Nets 101 Take an input Apply some filters Take output from filters Compute error Pass error backwards through filters Update filters Filters Output Target

Neural Nets 101 Feed Input Forward Input Filters Output Error Target

Neural Nets 101 Backpropagate Error Input Filters Error

Neural Nets 101 Backpropagate Error Input Updated Filters Error

Neural Nets 101 Next forward pass will have a smaller error (a more desirable result) Run this process on thousands or millions of images so that the network learns how to process a large number of different objects Input Updated Filters Error

Neural Nets 101 Generative Adversarial Networks Training the Generator Input Training the Generator Filters Real Input Generator Discriminator Filters Fake Real

Neural Nets 101 Generative Adversarial Networks Input Training the Discriminator Updated Filters Real Input Generator Discriminator Filters Fake Real

Neural Nets 101 Generative Adversarial Networks Input Training the Discriminator Filters Real Input Generator Discriminator Updated Filters Fake Real

http://tinyurl.com/ColourizerSurvey