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Photorealistic Image Colourization with Generative Adversarial Nets
Mathew Hall, Matthew Walker, Meghan Lele
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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
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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
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Neural Nets 101 Feed Input Forward Input Filters Output Error Target
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Neural Nets 101 Backpropagate Error Input Filters Error
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Neural Nets 101 Backpropagate Error Input Updated Filters Error
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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
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Neural Nets 101 Generative Adversarial Networks Training the Generator
Input Training the Generator Filters Real Input Generator Discriminator Filters Fake Real
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Neural Nets 101 Generative Adversarial Networks
Input Training the Discriminator Updated Filters Real Input Generator Discriminator Filters Fake Real
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Neural Nets 101 Generative Adversarial Networks
Input Training the Discriminator Filters Real Input Generator Discriminator Updated Filters Fake Real
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