@inproceedings{27d7c5b9617e439c9af55cfa2844aca0,
title = "An encoder generative adversarial network for multi-modality image recognition",
abstract = "This paper is concerned with the multi-modality image recognition which is a crucial technique used in industrial applications. The paper proposes a novel algorithm based on the deep generative adversarial network to learn a common feature space between different modalities. These abstract features are robust to the modality discrepancy and can be used to train a cross-modality classifier which will achieve excellent performance on all modalities. The comparative experiments on standard mul-ti-modality image recognition benchmark are employed to validate the effectiveness of our proposed algorithm. The results demonstrate that the proposed network is efficient to deal with the multi-modality recognition challenge, especially improve the performance on the modalities with limited training samples.",
keywords = "Deep learning, Generative adversarial network, Image recognition, Multi-modality",
author = "Yu Chen and Chunling Yang and Min Zhu and Yang, \{Shi Yan\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 ; Conference date: 20-10-2018 Through 23-10-2018",
year = "2018",
month = dec,
day = "26",
doi = "10.1109/IECON.2018.8591324",
language = "英语",
series = "Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2689--2694",
booktitle = "Proceedings",
address = "美国",
}