https://github.com/iitis/clusteringae
Autoencoders pretraining using clustering
https://github.com/iitis/clusteringae
Last synced: 7 months ago
JSON representation
Autoencoders pretraining using clustering
- Host: GitHub
- URL: https://github.com/iitis/clusteringae
- Owner: iitis
- License: gpl-3.0
- Created: 2021-12-03T13:10:54.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-12-13T10:25:36.000Z (over 4 years ago)
- Last Synced: 2025-02-18T14:24:20.498Z (over 1 year ago)
- Language: Python
- Size: 14.4 MB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## DESCRIPTION:
Autoencoders pretraining using clustering.
v.1.0
Related to the work:
> Improving Autoencoders Performance for Hyperspectral Unmixing using Clustering
> Source code for the review process of the 14th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2022).
## LICENSE:
Copyright 2021 Institute of Theoretical and Applied Informatics,
Polish Academy of Sciences (ITAI PAS)
Authors:
- Bartosz Grabowski (ITAI PAS, ORCID ID: [0000−0002−2364−6547](https://orcid.org/0000-0002-2364-6547))
- Przemysław Głomb (ITAI PAS, ORCID ID: [0000−0002−0215−4674](https://orcid.org/0000-0002-0215-4674)),
- Kamil Książek (ITAI PAS, ORCID ID: [0000−0002−0201−6220](https://orcid.org/0000-0002-0201-6220)),
- Krisztián Buza (Sapientia Hungarian University of Transylvania, ORCID ID: [0000-0002-7111-6452](https://orcid.org/0000-0002-7111-6452))
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
## FUNCTIONALITY:
- Autoencoder training and evaluation for spectral unmixing task
- Autoencoder pretraining using clustering algorithm
## FILES:
- `ATE/*`: [Autoencoder Testing Environment](https://github.com/iitis/AutoencoderTestingEnvironment) files.
- `cfg/*`: Config files.
- `grids/*.py`: Files required to run comparison between baseline and pretraining-based autoencoder training.
- `grids/run_exp.sh`: File to run comparison between baseline and pretraining-based autoencoder training.
- `grids/scripts/*`: Simple scripts used for various purposes.
- `grids/tests/*`: Unit tests.
## DATASETS:
All datasets have to be inserted into ./ATE/data/ folder.
## USAGE:
Run the script using `./grids/run_exp.sh` file.
The script requires Samson and Jasper datasets in the ./ATE/data/ folder as well as saved models' weights in mpath (set by default to ./models).
To run demo version of the experiment, run `./grids/run_exp_demo.sh` file. The results of the experiment will be generated in the `./results` directory. Please note that this version of the script uses Custom dataset which is composed of random numbers, so the results too are going to be random.
## DEPENDENCIES
The scripts are dependent on [Autoencoder Testing Environment](https://github.com/iitis/AutoencoderTestingEnvironment). Used datasets, as well as loaded models' weights follow the same structure.