science_cases:iwf_science_case
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- | We proposed a pipeline using a **UNet ** (Ronneberger et al., 2015) including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the ResUNet++ (Jha et al., 2019), for the automatic detection of ICMEs. Comparing it to our first results, we find that our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other data sets, as well as the three aligned data sets. The relatively fast training allows straightforward tuning of [[: | + | We proposed a pipeline using a **UNet ** (Ronneberger et al., 2015) including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the **< |
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+ | Results of this science case were presented at the {{: | ||
**References: | **References: | ||
- | * Nguyen, G., et al. (2019), Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, Astrophys. J. 874, 145, doi: | + | * Nguyen, G., et al. (2019), Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, Astrophys. J. 874, 145, doi: |
- | * Jha, D., et al. (2019), Resunet++: An advanced architecture for medical image segmentation, | + | * Jha, D., et al. (2019), Resunet++: An advanced architecture for medical image segmentation, |
- | * Ronneberger, | + | * Ronneberger, |
science_cases/iwf_science_case.1662978671.txt.gz · Last modified: 2022/09/12 12:31 by admin