Generative Adversarial Networks (GANs): a fun new framework for estimating generative models, introduced by Ian Goodfellow et al. We are using a 2-layer network from scalar to scalar (with 30 hidden units and tanh nonlinearities) for modeling both generator and discriminator network.
We are trying to reproduce this figure from the paper:
Except as a live demo. As we'll see below things are not quite as nice in practice:
true data distribution,
generator network from uniform noise in U[0,1],