Perceptual Losses for Real-Time Style Transfer and Super-Resolution · HackerPapers discussions

Perceptual Losses for Real-Time Style Transfer and Super-Resolution — mentioned by reiinakano

Mon, 02 Oct 2017 15:59:35 GMT

Ahh I see, these are some resources that should help you. The original Neural Style Transfer paper should help you understand the loss functions involved: https://arxiv.org/abs/1508.06576 The paper introducing Real-Time Style Transfer is basically the algorithm used here: https://arxiv.org/abs/1603.08155

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution — mentioned by nilkn

Thu, 06 Jul 2017 18:56:30 GMT

Slightly off-topic, but there's some pretty fascinating work that's been done recently on using neural networks for style transfer: https://arxiv.org/pdf/1603.08155v1.pdf http://www.anishathalye.com/2015/12/19/an-ai-that-can-mimic-...

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution — mentioned by chas

Fri, 10 Mar 2017 06:46:05 GMT

This approach surprised me. Why are they doing feature extraction and then feeding that into a DNN? It seems much more straightforward to have the input of the network be noisy samples and the output be clean samples a la super resolution[0] in images. They probably wouldn't want to use fully-connected layers in that instance, but I don't see any fundamental barriers if they have enough computational power to run a neural network already. Am I missing something? [0] https://arxiv.org/pdf/1603.08155.pdf

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution — mentioned by j2kun

Sun, 10 Jul 2016 07:07:24 GMT

IIRC you only get to choose from a fixed set of styles to process your image, right? Then they're probably using this algorithm to precompute the style model and seriously speed up rendering: http://arxiv.org/abs/1603.08155

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution — mentioned by dharma1

Mon, 13 Jun 2016 22:07:01 GMT

Try this, should be near realtime https://arxiv.org/abs/1603.08155

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