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Deep Convolutional Generative Adversarial Networks
- riordan 11y agoWell this is delightfully terrifying. Neural Networks that can create environments and people on their own.
- nazka 11y agoYa... Also they said that the code will be available today on the same repo (on the master branch, the current link is on another branch).
- gcr 11y agoThese models are just clever image uncompression. Nothing scary here. :) It's just like using Markov chains to make English-like gibberish, but for pictures. The interesting bit is that (I think) the generative adversarial network is regressing from random noise to an image, which isn't how most autoencoders work.
- manux 11y ago> These models are just clever image uncompression. Nothing scary here. :) Yes and no. The latent representation space can be seen as a kind of result of compression, but the opposite is going on. As you say, these models generate images from (latent space) random noise, but there's no direct mapping from image space to latent space (as there are with autoencoders), so no way to compress, really.
- murbard2 11y agoOf course there is: direct enumeration. If you insist on a practical way to do it, gradient descent on the latent vector would probably work well given the type of results DeepDream has obtained.
- gcr 11y agoI think variational autoencoders (which explicitly model the compression step and the decompression step) seem like the more classical way to do it.
- davmre 11y agoCompression is a subtle and powerful thing. The ability to compress is closely related to prediction: if you can predict 95% of the moves a chess grandmaster will make, you can compress the game by explicitly representing only the other 5% of moves. If you could perfectly predict (or equivalently, compress) the actions of a real-world human being, you'd have solved AI. Despite their simplicity, Markov chains are used throughout modern statistical AI, e.g., the Google Translate language model is essentially a big Markov chain. The fact that deep networks can apparently form better generative models across a wide range of applications (no one has ever actually gotten these kinds of image generation results from Markov chains) means that they really are getting at more interesting structure. They're not a panacea, but it's still a pretty big deal.
- frisco 11y agoPretty sure Google Translate is not a Markov model at this point; pretty sure it's a deep recurrent network.
- abrichr 11y agoYep, LSTM RNNs: http://googleresearch.blogspot.ca/2015/08/the-neural-networks-behind-google-voice.html?m=1 http://googleresearch.blogspot.ca/2015/08/the-neural-network...
- gcr 11y agoThe `master` branch is the one with the code. This link points to `gh-pages`, which doesn't have any. https://github.com/Newmu/dcgan_code/tree/master https://github.com/Newmu/dcgan_code/tree/master
- grandalf 11y agoI wonder if, with the right training data, this kind of thing could make use of detailed 3D/structural knowledge when doing the transforms. There is lots of information about the 3D world present in the light (at least for the bedrooms). This could be very useful for spatial navigation or computer vision, I'd think.
- nl 11y agoYes. I know a guy doing his PhD on this.
- grandalf 11y agoInteresting! Any links or terms to google would be appreciated.
- sedachv 11y agohttps://en.wikipedia.org/wiki/Geometric_feature_learning https://en.wikipedia.org/wiki/Geometric_feature_learning
- nl 11y agoI believe it is part of this project: https://cs.adelaide.edu.au/~hengel/?/research/project-lego/ https://cs.adelaide.edu.au/~hengel/?/research/project-lego/ Paper: http://arxiv.org/pdf/1511.02570.pdf http://arxiv.org/pdf/1511.02570.pdf He's in that research group, anyway.
- deepnet 11y agoThe Latent space of the image vectors is rich with emergent semantic algebra. Extracting a vector for glasses wearing and adding it to another person; rotating a face is a consistent vector; a vector for removing windows from any imagined room; a vector for smiling. This all seems a bit like Geoff Hinton's thought vectors - nice work.
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- cr4zy 11y agoLooks similar to the top project on this page from cs231n by Jon Gauthier: http://cs231n.stanford.edu/reports.html http://cs231n.stanford.edu/reports.html
- smhx 11y agoBoth use GANs to generate images, and that similarity is indeed there. Original GAN paper: http://papers.nips.cc/paper/5423-generative-adversarial-nets http://papers.nips.cc/paper/5423-generative-adversarial-nets
- acc12343 11y agoAnd this guy is an undergrad. Holy shit. This is the moment I am reminded of how much down the foodchain I really am.
- djfm 11y agoLooks very intersting (and beautiful), wish I could understand some more of it :) Could someone explain in simple terms how the arithmetic work? E.g. when they do "smiling woman" - "neutral woman" + "neutral man" = "smiling man", is it like you perform the operations on the networks' weights or something?
- smhx 11y agoThe input to the G-network is a Vector of numbers, of size K. The output is an image. Different random vectors give different output images. We are doing vector arithmetic in this input space, and the outputs seem to be semantically changing according to the vector arithmetic.
- michael_h 11y agoThose faces are right in the bottom trough of the uncanny valley. Especially the ones with the...teeth under the lips? Buh.
- temuze 11y agoI think AI-generated art is an industry waiting to happen. Imagine telling a VR system "show me a mountain ski resort with skiers going everywhere" and it just generates it. It could have a huge impact on procedurally generated maps in gaming. Or even plays or fiction! Look at how formulaic detective shows are. It'll probably be hard to get the emotional nuance, but maybe AI could make all of the characters and plots. While I think we're far away from generating TV shows with Conv-Nets, perhaps it can do simpler things (porn?).
- rl3 11y ago>It could have a huge impact on procedurally generated maps in gaming. I think it could go far beyond just maps. A major immersion breaker in most open-world games is the lack of detail in the lives of non-player characters. It's impractical to manually create this content at any real scale, so I think that's an area where AI would shine. However, that isn't terribly valuable without also being able to generate convincing conversation dynamically—and that's definitely Turing Test territory. It would also help to synthesize that output in the form of an uncanny and unique human voice, which is also hard, and probably squarely in the same territory. That said, I think all of this could be accomplished relatively soon, and without general intelligence necessarily being solved. Of course, once general intelligence is solved (and doesn't go sideways on us), it's a good bet that this kind of thing will be nothing short of incredible. I imagine the Holodeck from Star Trek might be a fair approximation. As an aside, utilizing general intelligence within interactive entertainment mediums for the purpose of creating believable characters may prove to be highly unethical. Bostrom's Superintelligence touched on this, and it's quite interesting if not terrifying to ponder.
- sedachv 11y ago> I think AI-generated art is an industry waiting to happen. It has been happening for a while: http://www.pamelamc.com/html/aaron_s_code.html http://www.pamelamc.com/html/aaron_s_code.html https://www.wordseye.com/ https://www.wordseye.com/ It would be interesting to see how you can do machine learning approaches for the kinds of descriptions WordsEye handles using rules, like "the man is in front of the wall." How do you learn to generate an image from that kind of description? You might need to have some kind of learning in abstract 3d space, you might not.
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