Semi-supervised Variational Autoencoder for WiFi Indoor Localization
We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencode...
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| Vydáno v: | International Conference on Indoor Positioning and Indoor Navigation s. 1 - 8 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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01.09.2019
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| ISSN: | 2471-917X |
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| Abstract | We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data. |
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| AbstractList | We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones. Our method is based on the variational au-toencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data. |
| Author | Antsfeld, Leonid Chidlovskii, Boris |
| Author_xml | – sequence: 1 givenname: Boris surname: Chidlovskii fullname: Chidlovskii, Boris organization: Naver Labs Europe,Meylan,France,38240 – sequence: 2 givenname: Leonid surname: Antsfeld fullname: Antsfeld, Leonid organization: Naver Labs Europe,Meylan,France,38240 |
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| PublicationTitle | International Conference on Indoor Positioning and Indoor Navigation |
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| Snippet | We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction... |
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| SubjectTerms | Buildings Data collection Deep learning Predictive models semi-supervised learning Semisupervised learning Task analysis UJI-IndoorLoc dataset variational auto-encoder WiFi based indoor localization Wireless fidelity |
| Title | Semi-supervised Variational Autoencoder for WiFi Indoor Localization |
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