Deep Growing Learning
Guangcong Wang, Xiaohua Xie, Jianhuang Lai, Jiaxuan Zhuo
Abstract
Semi-supervised learning (SSL) is an import paradigm to make full use of a large amount of unlabeled data in machine learning. A bottleneck of SSL is the overfitting problem when training over the limited labeled data, especially on a complex model like a deep neural network. To get around this bottleneck, we propose a bio-inspired SSL framework on deep neural network, namely Deep Growing Learning (DGL). Specifically, we formulate the SSL as an EM-like process, where the deep network alternately iterates between automatically growing convolutional layers and selecting reliable pseudo-labeled data for training. The DGL guarantees that a shallow neural network is trained with labeled data, while a deeper neural network is trained with growing amount of reliable pseudo-labeled data, so as to alleviate the overfitting problem. Experiments on different visual recognition tasks have verified the effectiveness of DGL.
BibTeX
@inproceedings{iccv2017_deepgrowinglearn,
title = {Deep Growing Learning},
author = {Guangcong Wang and Xiaohua Xie and Jianhuang Lai and Jiaxuan Zhuo},
booktitle = {ICCV 2017},
year = {2017}
}