{"id":21569155,"url":"https://github.com/kremerj/relabeling","last_synced_at":"2025-09-13T16:09:45.013Z","repository":{"id":92207447,"uuid":"125239311","full_name":"kremerj/relabeling","owner":"kremerj","description":"Code repository for the robust active label correction paper.","archived":false,"fork":false,"pushed_at":"2018-04-12T14:25:52.000Z","size":16330,"stargazers_count":10,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-18T05:44:21.872Z","etag":null,"topics":["active-learning","aistats-2018","convolutional-neural-networks","label-noise","logistic-regression"],"latest_commit_sha":null,"homepage":"","language":"Terra","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kremerj.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2018-03-14T16:07:34.000Z","updated_at":"2022-09-12T08:28:14.000Z","dependencies_parsed_at":"2023-06-08T00:06:22.846Z","dependency_job_id":null,"html_url":"https://github.com/kremerj/relabeling","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/kremerj/relabeling","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kremerj%2Frelabeling","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kremerj%2Frelabeling/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kremerj%2Frelabeling/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kremerj%2Frelabeling/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kremerj","download_url":"https://codeload.github.com/kremerj/relabeling/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kremerj%2Frelabeling/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274989928,"owners_count":25386552,"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","status":"online","status_checked_at":"2025-09-13T02:00:10.085Z","response_time":70,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["active-learning","aistats-2018","convolutional-neural-networks","label-noise","logistic-regression"],"created_at":"2024-11-24T11:06:55.676Z","updated_at":"2025-09-13T16:09:44.992Z","avatar_url":"https://github.com/kremerj.png","language":"Terra","readme":"# Robust Active Label Correction\n\nThis is the code repository complementing the paper\n\nJan Kremer, Fei Sha, and Christian Igel. [Robust Active Label Correction](http://proceedings.mlr.press/v84/kremer18a.html). *PMLR: Volume 84 (AISTATS)*, 2018\n\n```\n@inproceedings{Kremer18,\n  author    = {J. Kremer and F. Sha and C. Igel},\n  title     = {Robust Active Label Correction},\n  booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},\n  series    = {Proceedings of Machine Learning Research},\n  year      = 2018,\n  volume    = 84,\n  publisher = {PMLR}\n}\n```\n\nPlease cite us if you use any of the code provided here. All experiments from the paper can be reproduced from this repository. We use Python 3 and tensorflow 1.4. You can create and activate the conda environment by running\n\n```\nconda env create --file environment/relabeling.yml\nsource activate relabeling\n```\n\nor if you have GPU support\n\n```\nconda env create --file environment/relabeling-gpu.yml\nsource activate relabeling-gpu\n```\n\nTo get the necessary data and the pretrained [model weights](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/) for the CNN experiment, run\n\n```\nsh scripts/fetch_model.sh\n```\n\nand get the data (images and annotions) from http://bit.ly/2Duy6nK and should be unpacked into ```data/baidu```.\n\nThe necessary Cython code can be compiled by calling\n\n```\nsh scripts/compile.sh\n```\n\nThe logistic regression experiments can be reproduced by calling\n\n```\nsh scripts/relabeling.sh\n```\n\nThe results can be found in ```output/experiment/std```.\n\nThe CNN experiments can be reproduced by calling\n\n```\nsh scripts/relabeling_deep.sh\n```\n\nThe results can be found in ```output/experiment/deep```.\n\nAll plots can be generated by calling\n\n```\nsh scripts/generate_plots.sh\n```\n\nThe figures can be found in ```output/experiment/std/figures``` for the logistic regression experiments and in ```output/experiment/deep/figures``` for the CNN experiment.\n\nA single experiment can be run by calling\n\n```\npython relabeling.py\n```\n\nThe command-line help should guide you regarding available options.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkremerj%2Frelabeling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkremerj%2Frelabeling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkremerj%2Frelabeling/lists"}