{"id":15906685,"url":"https://github.com/csinva/local-vae","last_synced_at":"2026-04-26T23:32:03.074Z","repository":{"id":97069811,"uuid":"294629609","full_name":"csinva/local-vae","owner":"csinva","description":"Making locally disentangled vaes.","archived":false,"fork":false,"pushed_at":"2021-02-12T21:33:07.000Z","size":106866,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-02T23:23:02.075Z","etag":null,"topics":["disentanglement","neural-network","pytorch","vae"],"latest_commit_sha":null,"homepage":"https://csinva.io/local-vae/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/csinva.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":"2020-09-11T07:49:14.000Z","updated_at":"2023-03-10T02:16:28.000Z","dependencies_parsed_at":"2023-03-13T16:19:35.733Z","dependency_job_id":null,"html_url":"https://github.com/csinva/local-vae","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/csinva/local-vae","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Flocal-vae","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Flocal-vae/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Flocal-vae/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Flocal-vae/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/csinva","download_url":"https://codeload.github.com/csinva/local-vae/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csinva%2Flocal-vae/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32317164,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T23:26:28.701Z","status":"ssl_error","status_checked_at":"2026-04-26T23:26:25.802Z","response_time":129,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["disentanglement","neural-network","pytorch","vae"],"created_at":"2024-10-06T13:41:17.822Z","updated_at":"2026-04-26T23:32:03.058Z","avatar_url":"https://github.com/csinva.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Trying to make locally disentangled VAEs.\n\n*This repo is actively maintained. For any questions please file an issue.*\n\n\n# related work\n- TRIM (ICLR 2020 workshop [pdf](https://arxiv.org/abs/2003.01926), [github](https://github.com/csinva/transformation-importance)) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)\n- ACD (ICLR 2019 [pdf](https://openreview.net/pdf?id=SkEqro0ctQ), [github](https://github.com/csinva/hierarchical-dnn-interpretations)) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy\n- CDEP (ICML 2020 [pdf](https://arxiv.org/abs/1909.13584), [github](https://github.com/laura-rieger/deep-explanation-penalization)) - penalizes CD / ACD scores during training to make models generalize better\n- DAC (arXiv 2019 [pdf](https://arxiv.org/abs/1905.07631), [github](https://github.com/csinva/disentangled-attribution-curves)) - finds disentangled interpretations for random forests\n- PDR framework (PNAS 2019 [pdf](https://arxiv.org/abs/1901.04592)) - an overarching framewwork for guiding and framing interpretable machine learning\n\n# reference\n\n- feel free to use/share this code openly\n- uses code from [disentangling-vae](https://github.com/YannDubs/disentangling-vae) + [TRIM](https://github.com/csinva/transformation-importance)\n- if you find this code useful for your research, please cite the following:\n\n```r\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Flocal-vae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsinva%2Flocal-vae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Flocal-vae/lists"}