Besides a cool new application of GANNs, I don't see if this architecture is much different than normal GANNs. Anyone else have thoughts?
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Image-to-Image Translation with Conditional Adversarial Nets
41–50 of 61 posts
Re: Image-to-Image Translation with Conditional Adversarial Nets
#42Kudos for providing proper examples of the network doing its thing, both good and bad. This is what all researched ought to do. Too many papers these days handpick a couple coolest looking results and stop at that.
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I get a feeling this could be used in game design to do some really cool stuff with map and texture generation.
Re: Image-to-Image Translation with Conditional Adversarial Nets
#43Kudos for providing proper examples of the network doing its thing, both good and bad. This is what all researched ought to do. Too many papers these days handpick a couple coolest looking results and stop at that. ... I get a feeling this could be used in game design to do some really cool stuff with map and texture generation.
It could reduce the game size way down if it can generate textures on-the-fly.
Re: Image-to-Image Translation with Conditional Adversarial Nets
#44Does anyone else have the feeling that with the current trajectory, something exactly like this, but with perhaps a million times the amount of feedback and data, thought will just emerge ? Yes, this is all 2D and abstract/selective training sets etc, but what if AI is the ultimate fake-it-until-you-make-it?
I don't see this happening. What I do see happening, is it figuring us out. Somewhere out there, there's a function which explains how exactly our society is completely organized in every way. From that, the AI could generate books, movies, and do a lot of things.
Hoo boy, buddy, do I got some news for you: https://www.marxists.org/archive/marx/works/1867-c1/
Re: Image-to-Image Translation with Conditional Adversarial Nets
#45This is awesome! Makes me wonder how this can apply to image and video compression. You could send over the semantic segmentation version of an image or video, and system on the other end would use these technique to reconstruct the original.
Here is my work, where I do use semantic information to achieve compression (rather improve JPEG). This is not an end to end compression like Google's work, but just incorporating semantic knowledge into compression. I am still trying to clean up the code before I make arxiv/github submission, but since you are interested here is the link http://gpgpu.cs-i.brandeis.edu/semantic_jpeg.pdf
Re: Image-to-Image Translation with Conditional Adversarial Nets
#46Does anyone else have the feeling that with the current trajectory, something exactly like this, but with perhaps a million times the amount of feedback and data, thought will just emerge ? Yes, this is all 2D and abstract/selective training sets etc, but what if AI is the ultimate fake-it-until-you-make-it?
I don't see this happening. What I do see happening, is it figuring us out. Somewhere out there, there's a function which explains how exactly our society is completely organized in every way. From that, the AI could generate books, movies, and do a lot of things.
Reminds me of the novel-rewriting-apparatus from 1984, except with more friggin' superheroes and remakes.
Re: Image-to-Image Translation with Conditional Adversarial Nets
#47I'm interested in having a play. As an out and out ML newbie, is there such a thing as an AWS image I could run on a GPU instance and then just git clone and go?
Try one of the bitfusion AMIs on a g2.2xlarge instance.
Thanks very much. If anyone else is interested I can confirm that the Bitfusion Boost Ubuntu 14 Torch 7 AMI on a g2.2xlarge instance does offer a relatively painless way to get going with this, although I couldn't get the python image combiner to work so had prepare those separately. Have just trained my first neural net, most exciting!
Re: Image-to-Image Translation with Conditional Adversarial Nets
#48Truly impressive overall. Unfortunately, it looks like training set was way too small. Look for example at reconstruction of #13 here:
https://phillipi.github.io/pix2pix/images/index_facades2_los...
Notice white triangles (image crop artifacts) present on the original image, yet completely absent on the net input image. They make re-appearance on the output of 3 (4 even?) out of 5 nets despite the lack of corresponding cue in the input image. Looks like network cheated a bit here, i.e. took advantage of small set size and memorized the input image as a whole. Then recognized and recalled this very image (already seen during training) rather than actually reconstructing it purely from the input.
Same (but less prominent) for other images where "ground truth" image was cropped.
Re: Image-to-Image Translation with Conditional Adversarial Nets
#49Neural nets! Is there anything they can't do?
Re: Image-to-Image Translation with Conditional Adversarial Nets
#50Does anyone else have the feeling that with the current trajectory, something exactly like this, but with perhaps a million times the amount of feedback and data, thought will just emerge ? Yes, this is all 2D and abstract/selective training sets etc, but what if AI is the ultimate fake-it-until-you-make-it?
I don't think it's going to emerge without significant effort to make it happen. I think most of the 'intelligence' we desire will be attainable without sentience. Sentience itself will require a lot of specific research directed at the goal. It's certainly a risk though.