Data Augmentation with Adversarial Autoencoders for the Clustering of Electromagnetic Interference Signals
Authors: 
Di Zhao, Wei Chen, Hongyi LiAuthors Info & Claims
Di Zhao, Wei Chen, Hongyi LiAuthors Info & ClaimsPages 111 - 115
Abstract
The widespread application of telecommunication technologies urgently calls for an effective method to analyze the interferences in signals. To utilize machine learning algorithms to discern different types of EMI, several models have been proposed in the past, unfortunately almost all of which suffer from the scarcity of training data. In this paper, we propose a method to augment the training data set. With data generated by pre-trained adversarial autoencoders, models are enabled to perform better across multiple metrics.
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- Data Augmentation with Adversarial Autoencoders for the Clustering of Electromagnetic Interference Signals
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Published In
November 2020
1202 pages
ISBN:9781450387811
DOI:10.1145/3443467
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Published: 01 February 2021
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EITCE 2020
EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering
November 6 - 8, 2020
Xiamen, China
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EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;
Overall Acceptance Rate 508 of 972 submissions, 52%
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