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HoloGAN: Unsupervised learning of 3D representations from natural images. Another demonstration that hardcoding prior knowledge about the world is generally a good idea. Blog: https://www.monkeyoverflow.com/#/hologan-unsupervised-learning-of-3d-representations-from-natural-images/ … Paper: https://arxiv.org/abs/1904.01326 pic.twitter.com/7Mpkz2rOjm
They show that, when you explicitly impose 3D transformations in the generator pipeline (acting on a 3D-latent representation), the model automatically learns to disentangle pose from identity. Also: the AdaIN layer strikes again!pic.twitter.com/UwQHQ7PYmH
Can you clarify: “is generally a good idea” or “is generally *not* a good idea”?
We know that the world contains 3-Dimensional objects. This paper shows that leveraging this knowledge by hardcoding it into the network architecture results in a model that automatically learns a satisfying property: disentangling object pose from object identity.
And this is a recurring trend in model building: if you have strong prior knowledge about your problem domain, hardcoding those priors into your model usually pays off!
This is really cool! Also, a good counter argument to the "Bitter Lesson" http://www.incompleteideas.net/IncIdeas/BitterLesson.html … that modelling never pays off.
I’d say it’s a bitter-sweet lesson: https://www.marktechpost.com/2019/03/26/artificial-intelligence-a-bitter-sweet-symphony-in-modelling/ … ;)
Great work! I also have some more evidence on the use of prior operators: https://arxiv.org/abs/1712.00374
Nice - except I wanted to use that name for my holography GANs
This looks great! Love to see more domain-specifics coming into this sort of thing. Hard to anticipate whether 3D features would make training faster or slower, need to read more. Also, that grid of "cats saying no" seems sure to find fun applications
They have such a cool blog!
I would love to watch some CGI or play a game rendered this way, like a AI version of rotoscoping/"Waking Life" unreality But yeah, otherwise not so useful
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