Deep Growing Learning

Guangcong Wang, Xiaohua Xie, Jianhuang Lai, Jiaxuan Zhuo; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2812-2820

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2017_ICCV,
author = {Wang, Guangcong and Xie, Xiaohua and Lai, Jianhuang and Zhuo, Jiaxuan},
title = {Deep Growing Learning},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}