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Proceedings Paper

Semi-supervised adversarial training of a lightweight neural network for visual recognition
Author(s): Kaan Karaman; Ibrahim Batuhan Akkaya
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Paper Abstract

Deep learning is a widely utilized approach specifically for computer vision applications. Visual recognition is one of the applications utilizing deep learning. Several challenges limit the performance of visual recognition methods. One of the most important challenges is the insufficient number of labeled data in the datasets. To overcome this challenge, the recent studies propose sophisticated methods which require high computational resources, which may create another problem. That is, the implementation of such algorithms on mobile devices is quite challenging. Especially, these issues are encountered in surveillance systems that utilize the drones and/or CC-TVs. To solve these problems and obtain high accuracy, the network should be able to extract both representative and discriminative features from such a small amount of data. In this paper, we propose a generative adversarial semi-supervised training method for visual recognition. Experiments are performed to evaluate a lightweight deep convolutional neural network as a classifier network that is trained by the proposed method and a conditional/unconditional generator networks that are examined in adversarial training.

Paper Details

Date Published: 7 October 2019
PDF: 10 pages
Proc. SPIE 11166, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III, 111660O (7 October 2019); doi: 10.1117/12.2533456
Show Author Affiliations
Kaan Karaman, ASELSAN A.S. (Turkey)
Ibrahim Batuhan Akkaya, ASELSAN A.S. (Turkey)

Published in SPIE Proceedings Vol. 11166:
Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III
Henri Bouma; Radhakrishna Prabhu; Robert James Stokes; Yitzhak Yitzhaky, Editor(s)

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