Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis · HackerPapers discussions

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — mentioned by fitzwatermellow

Fri, 30 Sep 2016 14:14:19 GMT

Great work! The fact that it captures "long-range order" seemingly perfectly is something not many have been able to do before! And the "collapse" visualization is great fun to watch. But is your algorithm really qualitatively all that different from previous search methods (e.g. Efros and Leung), if you are still (uniform random?) sampling over the input distribution of patches? I notice also your input textures tend to have sharp boundaries (as is common in pixel art). It would be interesting to see the results when the input has a lot of "noise", such as high def images of rocks or clouds…

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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — mentioned by visarga

Fri, 08 Apr 2016 05:02:23 GMT

Take a look at these: * Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (http://arxiv.org/abs/1601.04589) * A Neural Algorithm of Artistic Style (http://arxiv.org/abs/1508.06576) They do much more than photoshopping. It's actual synthesis of new images in the style of a desired painter.

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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — mentioned by awentzonline

Fri, 11 Mar 2016 05:22:35 GMT

According to their about page, that site is using this algorithm http://arxiv.org/pdf/1508.06576.pdf whereas neural-image-analogies is a combination of http://www.mrl.nyu.edu/projects/image-analogies/index.html and http://arxiv.org/pdf/1601.04589v1.pdf Basically you can do a few other things with image analogies, such as texture-by-numbers, that the neural style algo doesn't cover. In addition, the matching and blending covered in the Li and Wand paper provides more accurate style details.

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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — mentioned by awentzonline

Mon, 07 Mar 2016 13:46:23 GMT

That's pretty cool. The patch matching in neural image analogies is based on this: http://arxiv.org/pdf/1601.04589v1.pdf

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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — mentioned by jbmorgado

Wed, 02 Mar 2016 14:27:33 GMT

This actually seems to come from a new paper: http://arxiv.org/pdf/1601.04589v1.pdf

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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — HN submission

Thu, 28 Jan 2016 23:19:58 GMT

Transfer Image Style -Combining Markov Random Fields and CNN for Image Synthesis

2 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — HN submission

Fri, 22 Jan 2016 11:26:15 GMT

Combining Markov Random Fields and Convolutional Neural Networks for Image Synth

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis — HN submission

Wed, 20 Jan 2016 22:07:26 GMT

Combining Markov Random Fields and Convolutional Neural Nets for Image Synthesis

4 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.