MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
Abstract
We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network’s performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.
Cite
Text
Gordon et al. "MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.Markdown
[Gordon et al. "MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/gordon2018cvpr-morphnet/)BibTeX
@inproceedings{gordon2018cvpr-morphnet,
title = {{MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks}},
author = {Gordon, Ariel and Eban, Elad and Nachum, Ofir and Chen, Bo and Wu, Hao and Yang, Tien-Ju and Choi, Edward},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
url = {https://mlanthology.org/cvpr/2018/gordon2018cvpr-morphnet/}
}