{"id":20669536,"url":"https://github.com/iitis/clusteringae","last_synced_at":"2025-11-07T01:04:25.303Z","repository":{"id":141460310,"uuid":"434600821","full_name":"iitis/ClusteringAE","owner":"iitis","description":"Autoencoders pretraining using clustering","archived":false,"fork":false,"pushed_at":"2021-12-13T10:25:36.000Z","size":15067,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-18T14:24:20.498Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iitis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-12-03T13:10:54.000Z","updated_at":"2021-12-16T04:22:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"65fe7873-8718-4f20-9ff3-4ed01c7a8a26","html_url":"https://github.com/iitis/ClusteringAE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FClusteringAE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FClusteringAE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FClusteringAE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FClusteringAE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iitis","download_url":"https://codeload.github.com/iitis/ClusteringAE/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242884554,"owners_count":20201131,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-16T20:14:44.647Z","updated_at":"2025-11-07T01:04:25.260Z","avatar_url":"https://github.com/iitis.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## DESCRIPTION:\nAutoencoders pretraining using clustering.\n\nv.1.0\n\nRelated to the work:\n\u003e Improving Autoencoders Performance for Hyperspectral Unmixing using Clustering\n\n\u003e Source code for the review process of the 14th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2022).\n\n## LICENSE:\nCopyright 2021 Institute of Theoretical and Applied Informatics,\nPolish Academy of Sciences (ITAI PAS) \u003chttps://www.iitis.pl\u003e\nAuthors:\n- Bartosz Grabowski (ITAI PAS, ORCID ID: [0000−0002−2364−6547](https://orcid.org/0000-0002-2364-6547))\n- Przemysław Głomb (ITAI PAS, ORCID ID: [0000−0002−0215−4674](https://orcid.org/0000-0002-0215-4674)),\n- Kamil Książek (ITAI PAS, ORCID ID: [0000−0002−0201−6220](https://orcid.org/0000-0002-0201-6220)),\n- Krisztián Buza (Sapientia Hungarian University of Transylvania, ORCID ID: [0000-0002-7111-6452](https://orcid.org/0000-0002-7111-6452))\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u003chttps://www.gnu.org/licenses/\u003e.\n\n## FUNCTIONALITY:\n- Autoencoder training and evaluation for spectral unmixing task\n- Autoencoder pretraining using clustering algorithm\n\n## FILES:\n- `ATE/*`: [Autoencoder Testing Environment](https://github.com/iitis/AutoencoderTestingEnvironment) files.\n- `cfg/*`: Config files.\n- `grids/*.py`: Files required to run comparison between baseline and pretraining-based autoencoder training.\n- `grids/run_exp.sh`: File to run comparison between baseline and pretraining-based autoencoder training.\n- `grids/scripts/*`: Simple scripts used for various purposes.\n- `grids/tests/*`: Unit tests.\n\n## DATASETS:\nAll datasets have to be inserted into \u003cem\u003e./ATE/data/\u003c/em\u003e folder.\n\n## USAGE:\nRun the script using `./grids/run_exp.sh` file.\nThe script requires Samson and Jasper datasets in the \u003cem\u003e./ATE/data/\u003c/em\u003e folder as well as saved models' weights in \u003cem\u003empath\u003c/em\u003e (set by default to \u003cem\u003e./models\u003c/em\u003e).\n\nTo 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.\n\n## DEPENDENCIES\nThe scripts are dependent on [Autoencoder Testing Environment](https://github.com/iitis/AutoencoderTestingEnvironment). Used datasets, as well as loaded models' weights follow the same structure.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Fclusteringae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiitis%2Fclusteringae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Fclusteringae/lists"}