Semi-supervised novelty detection in opportunistic science missions using variational autoencoders
Scientific opportunities are missed in planetary explorations due to the lack of communication and/or long-time communication delays between rovers and ground stations. By enabling rovers to autonomously detect and explore targets the overall scientific outcome of extraterrestrial missions can be in...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
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British Machine Vision Association and Society for Pattern Recognition (BMVA)
2020
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| _version_ | 1797092991580504064 |
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| author | Sintini, L Kunze, L |
| author_facet | Sintini, L Kunze, L |
| author_sort | Sintini, L |
| collection | OXFORD |
| description | Scientific opportunities are missed in planetary explorations due to the lack of communication and/or long-time communication delays between rovers and ground stations. By enabling rovers to autonomously detect and explore targets the overall scientific outcome of extraterrestrial missions can be increased.
In this paper, we have designed, developed, and evaluated unsupervised as well as semi-supervised approaches to novelty detection based on Variational Autoencoders (VAE). Our VAE model was trained on typical data from previous missions and tested to
infer the novelty of scientific targets. In an ablation study, we investigate the effectiveness of different types of loss functions. We compare losses based on reconstruction errors, losses obtained from the VAE’s latent space as well as a combination of both. In our experiments, we have evaluated both unsupervised and semi-supervised approaches on datasets obtained from NASA’s Mars Curiosity rover. Results show that our VAE-based approaches are not only robust but also comparable, or better, than the state-of-the-art.
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| first_indexed | 2024-03-07T03:53:56Z |
| format | Conference item |
| id | oxford-uuid:c235a6f2-311f-492c-8634-007bb3d020e2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:53:56Z |
| publishDate | 2020 |
| publisher | British Machine Vision Association and Society for Pattern Recognition (BMVA) |
| record_format | dspace |
| spelling | oxford-uuid:c235a6f2-311f-492c-8634-007bb3d020e22022-03-27T06:07:20ZSemi-supervised novelty detection in opportunistic science missions using variational autoencodersConference itemhttp://purl.org/coar/resource_type/c_5794uuid:c235a6f2-311f-492c-8634-007bb3d020e2EnglishSymplectic ElementsBritish Machine Vision Association and Society for Pattern Recognition (BMVA)2020Sintini, LKunze, LScientific opportunities are missed in planetary explorations due to the lack of communication and/or long-time communication delays between rovers and ground stations. By enabling rovers to autonomously detect and explore targets the overall scientific outcome of extraterrestrial missions can be increased. In this paper, we have designed, developed, and evaluated unsupervised as well as semi-supervised approaches to novelty detection based on Variational Autoencoders (VAE). Our VAE model was trained on typical data from previous missions and tested to infer the novelty of scientific targets. In an ablation study, we investigate the effectiveness of different types of loss functions. We compare losses based on reconstruction errors, losses obtained from the VAE’s latent space as well as a combination of both. In our experiments, we have evaluated both unsupervised and semi-supervised approaches on datasets obtained from NASA’s Mars Curiosity rover. Results show that our VAE-based approaches are not only robust but also comparable, or better, than the state-of-the-art. |
| spellingShingle | Sintini, L Kunze, L Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title | Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title_full | Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title_fullStr | Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title_full_unstemmed | Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title_short | Semi-supervised novelty detection in opportunistic science missions using variational autoencoders |
| title_sort | semi supervised novelty detection in opportunistic science missions using variational autoencoders |
| work_keys_str_mv | AT sintinil semisupervisednoveltydetectioninopportunisticsciencemissionsusingvariationalautoencoders AT kunzel semisupervisednoveltydetectioninopportunisticsciencemissionsusingvariationalautoencoders |