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SMASH: One-Shot Model Architecture Search through HyperNetworks
Andrew Brock, Theo Lim, J.M. Ritchie, Nick Weston
Feb 15, 2018 (modified: Feb 24, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract: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.
TL;DR:A technique for accelerating neural architecture selection by approximating the weights of each candidate architecture instead of training them individually.
Keywords:meta-learning, architecture search, deep learning, computer vision
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