Generative models

In the context of neural networks, generative models refers to those networks which output images. We’ve seen Deepdream and style transfer already, which can also be regarded as generative, but in contrast, those are produced by an optimization process in which convolutional neural networks are merely used as a sort of analytical tool. In generative models like autoencoders and generative adversarial networks, the convnets output the images themselves. This chapter will look at those two specifically.


So far, we’ve mostly interpreted neural networks as being predictive, i.e. given some inputs, what is the output of – where it’s going, etc. But this is just a special case of a much more general capacity they have.



interesting property of DCGANs

hardmaru - GAN + VRAE making

img: generative models: whats wrong with auto encoders’s-wrong-with-autoencoders.html

unreasonable confusion of VAEs

text to image

GANS explained

deep image completion seeing beyond edges of image

transfiguring portraits:

soumith + yann

end to end neural style with gans

eyescream eyescream

generating faces with torch



Fast Scene Understanding with Generative Models (nice video)

hardmaru images from latent vectors (GAN + VAE)

autoencoders book chapter

gen models - describe probability distributions and data manifolds 2d line manifold, or plane in 3d, then go to eigenfaces neural net modeling prob distribution very sparse

we sometimes use the word astronomical to describe very large quantities. but no nimberassociated with astronmy, like the number of atoms in the universe, even begins to approach ___

GAN papers