hanboa / hypergan

Generative Model for Neural Networks

HyperGAN: A Generative Model for Neural Networks

HyperGAN generates fully trained neural networks simply by sampling from noise. No large dataset of fully trained examples needed. We use repeated MLE estimates to bring the generators closer to generating samples from the true distribution In the end, we have fully trained neural networks that can be instantly generated. Large ensembles are trivial to create and expand.

Generating a huge ensemble (100-1000 or more networks) allows us to fully approximate the learned distribution of neural networks. Allowing us to make advances in anomaly detection and uncertainty estimation.

Train a HyperGAN

1-D Regression

python3 hypertoy_regression.py --pretrain_e True

MNIST

python3 hypermnist.py --pretrain_e True 

MNIST 5000

python3 hypermnist5000.py --pretrain_e True

CIFAR-10

python3 hypercifar.py --pretrain_e True

CIFAR-10 5000

python3 hypercifar5000.py --pretrain_e True