An Encoder Generative Adversarial Network For Multi-Modality Image Recognition
IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY(2018)
摘要
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 multi- 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.
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关键词
image recognition, multi-modality, deep learning, generative adversarial network
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