Hypernetwork-Based Implicit Posterior Estimation and Model Averaging of CNN
Abstract
Deep neural networks have a rich ability to learn complex representations and achieved remarkable results in various tasks. However, they are prone to overfitting due to the limited number of training samples; regularizing the learning process of neural networks is critical. In this paper, we propose a novel regularization method, which estimates parameters of a large convolutional neural network as implicit probabilistic distributions generated by a hypernetwork. Also, we can perform model averaging to improve the network performance. Experimental results demonstrate our regularization method outperformed the commonly-used maximum a posterior (MAP) estimation.
Cite
Text
Ukai et al. "Hypernetwork-Based Implicit Posterior Estimation and Model Averaging of CNN." Proceedings of The 10th Asian Conference on Machine Learning, 2018.Markdown
[Ukai et al. "Hypernetwork-Based Implicit Posterior Estimation and Model Averaging of CNN." Proceedings of The 10th Asian Conference on Machine Learning, 2018.](https://mlanthology.org/acml/2018/ukai2018acml-hypernetworkbased/)BibTeX
@inproceedings{ukai2018acml-hypernetworkbased,
title = {{Hypernetwork-Based Implicit Posterior Estimation and Model Averaging of CNN}},
author = {Ukai, Kenya and Matsubara, Takashi and Uehara, Kuniaki},
booktitle = {Proceedings of The 10th Asian Conference on Machine Learning},
year = {2018},
pages = {176-191},
volume = {95},
url = {https://mlanthology.org/acml/2018/ukai2018acml-hypernetworkbased/}
}