SMASH: One-Shot Model Architecture Search through HyperNetworks - implementation -

Today, we have a video, preprint and an implementation, what else can you ask for ?

SMASH: One-Shot Model Architecture Search through HyperNetworks by Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston

Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL

An implementation of SMASH is here: https://github.com/ajbrock/SMASH
h/t Andrew, GitXiv


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