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* They suggest a new architecture for GANs. * Their architecture adds another Generator for a reverse branch (from images to noise vector `z`). * Their architecture takes some ideas from VAEs/variational neural nets. * Overall they can improve on the previous state of the art (DCGAN). ### How * Architecture * Usually, in GANs one feeds a noise vector `z` into a Generator (G), which then generates an image (`x`) from that noise. * They add a reverse branch (G2), in which another Generator takes a real image (`x`) and generates a noise vector `z` from that. * The noise vector can now be viewed as a latent space vector. * Instead of letting G2 generate *discrete* values for `z` (as it is usually done), they instead take the approach commonly used VAEs and use *continuous* variables instead. * That is, if `z` represents `N` latent variables, they let G2 generate `N` means and `N` variances of gaussian distributions, with each distribution representing one value of `z`. * So the model could e.g. represent something along the lines of "this face looks a lot like a female, but with very low probability could also be male". * Training * The Discriminator (D) is now trained on pairs of either `(real image, generated latent space vector)` or `(generated image, randomly sampled latent space vector)` and has to tell them apart from each other. * Both Generators are trained to maximally confuse D. * G1 (from `z` to `x`) confuses D maximally, if it generates new images that (a) look real and (b) fit well to the latent variables in `z` (e.g. if `z` says "image contains a cat", then the image should contain a cat). * G2 (from `x` to `z`) confuses D maximally, if it generates good latent variables `z` that fit to the image `x`. * Continuous variables * The variables in `z` follow gaussian distributions, which makes the training more complicated, as you can't trivially backpropagate through gaussians. * When training G1 (from `z` to `x`) the situation is easy: You draw a random `z`vector following a gaussian distribution (`N(0, I)`). (This is basically the same as in "normal" GANs. They just often use uniform distributions instead.) * When training G2 (from `x` to `z`) the situation is a bit harder. * Here we need to use the reparameterization trick here. * That roughly means, that G2 predicts the means and variances of the gaussian variables in `z` and then we draw a sample of `z` according to exactly these means and variances. * That sample gives us discrete values for our backpropagation. * If we do that sampling often enough, we get a good approximation of the true gradient (of the continuous variables). (Monte Carlo approximation.) * Results * Images generated based on CelebA dataset: * ![CelebA samples](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/Adversarially_Learned_Inference__celebasamples.png?raw=true "CelebA samples") * Left column per pair: Real image, right column per pair: reconstruction (`x > z` via G2, then `z > x` via G1) * ![CelebA reconstructions](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/Adversarially_Learned_Inference__celebareconstructions.png?raw=true "CelebA reconstructions") * Reconstructions of SVHN, notice how the digits often stay the same, while the font changes: * ![SVHN reconstructions](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/Adversarially_Learned_Inference__svhnreconstructions.png?raw=true "SVHN reconstructions") * CIFAR10 samples, still lots of errors, but some quite correct: * ![CIFAR10 samples](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/Adversarially_Learned_Inference__cifar10samples.png?raw=true "CIFAR10 samples")
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