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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" |
Identification Number: |
doi:10.18419/darus-2174 |
Distributor: |
DaRUS |
Date of Distribution: |
2021-10-05 |
Version: |
1 |
Bibliographic Citation: |
Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1 |
Citation |
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Title: |
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" |
Identification Number: |
doi:10.18419/darus-2174 |
Authoring Entity: |
Tkachev, Gleb (Universität Stuttgart) |
Grant Number: |
EXC 2075 - 390740016 |
Distributor: |
DaRUS |
Access Authority: |
Tkachev, Gleb |
Access Authority: |
Frey, Steffen |
Depositor: |
Tkachev, Gleb |
Date of Deposit: |
2021-09-29 |
Holdings Information: |
https://doi.org/10.18419/darus-2174 |
Study Scope |
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Keywords: |
Computer and Information Science, Visualization, Machine Learning, Spatiotemporal Data, Ensemble Visualization, Visual Exploration |
Abstract: |
We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details. |
Notes: |
<p>We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper.</p> <p>To run it, you will need:</p> <ul> <li>Any modern Linux distribution. Tested on CentOS 7.0</li> <li>Singularity<br/> An HPC-focused containerization tool.<br/> Available as a package or can be built from source.</br> You will need root to build the S4 container image (see the README).</li> <li>NVIDIA drivers.<br/> The code uses Tensorflow and CUDA, so an NVIDIA GPU is needed.<br/> The driver cannot be containerized with Singularity, so it should be installed on the host.</li> <p>Everything else is installed automatically inside the container.</p> <p>To build and run the code, you will run the `build-and-reproduce.sh` script:</p> <pre> cd S4 chmod u+x build-and-reproduce.sh && ./build-and-reproduce.sh</pre> </p>You might need to comment out the lines calling `git` if you downloaded the code without using git.<br/> If you run into problems, follow the detailed instructions in the README.md</p> |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
G. Tkachev, S. Frey and T. Ertl, "S4: Self-Supervised learning of Spatiotemporal Similarity," in IEEE Transactions on Visualization and Computer Graphics |
Identification Number: |
10.1109/TVCG.2021.3101418 |
Bibliographic Citation: |
G. Tkachev, S. Frey and T. Ertl, "S4: Self-Supervised learning of Spatiotemporal Similarity," in IEEE Transactions on Visualization and Computer Graphics |
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build-and-reproduce.sh |
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environment.yml |
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LICENSE |
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reproduce-figures.sh |
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_README.md |
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200414_cylinder-300-2-basic-paper.py |
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200817_cylinder-300-2-basic_all-metrics.py |
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debug.py |
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droplet-splash-basic.py |
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classifier-basic.py |
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encoder-3d-basic.py |
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encoder-basic.py |
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encoder-vgg.py |
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cylinder-ensemble.zip |
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cylinder-turbulent-query.png |
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droplet-splashing.png |
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haikunator.py |
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BufferedNdArray.py |
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config_tools.py |
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CppDispatcherBase.py |
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CppWrapper.py |
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file_tools.py |
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iterator_tools.py |
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KerasBatchedTrainer.py |
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KerasCheckpointCallback.py |
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keras_extras.py |
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logging_tools.py |
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MtPatchExtractor.py |
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volume_tools.py |
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__init__.py |
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test_BufferedNdArray.py |
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test_MtPatchExtractor.py |
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test_Normalizer.py |
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test_numpy_extras.py |
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test_numpy_extras_4d_patching.py |
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BufferedNdArrayWrapper.cpp |
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dllmain.cpp |
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MtPatchExtractorWrapper.cpp |
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PythonExtrasC.cpp |
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RenderingTools.cpp |
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macros.h |
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PythonExtrasCLib.cpp |
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coord_mapping.py |
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data_loading.py |
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data_types.py |
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feature_matching.py |
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feature_processing.py |
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layers.py |
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ranking_metrics.py |
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siamese.py |
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vgg_keras.py |
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server.py |
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ensemble_figure_tools.py |
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export_paper_csv.py |
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paper_aliases.py |
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