{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adjusting-for-confounding-in-unsupervised","title":"Adjusting for Confounding in Unsupervised Latent Representations of Images","arxiv_id":"1811.06498","date":"2018-11-15","proceeding":null,"authors":["Craig A. Glastonbury","Michael Ferlaino","Christoffer Nellåker","Cecilia M. Lindgren"],"abstract":"Biological imaging data are often partially confounded or contain unwanted\nvariability. Examples of such phenomena include variable lighting across\nmicroscopy image captures, stain intensity variation in histological slides,\nand batch effects for high throughput drug screening assays. Therefore, to\ndevelop \"fair\" models which generalise well to unseen examples, it is crucial\nto learn data representations that are insensitive to nuisance factors of\nvariation. In this paper, we present a strategy based on adversarial training,\ncapable of learning unsupervised representations invariant to confounders. As\nan empirical validation of our method, we use deep convolutional autoencoders\nto learn unbiased cellular representations from microscopy imaging.","url_abs":"http://arxiv.org/abs/1811.06498v2","url_pdf":"http://arxiv.org/pdf/1811.06498v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adjusting-for-confounding-in-unsupervised","repo_url":"https://github.com/Nellaker-group/FairUnsupervisedRepresentations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.06498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06498"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Nellaker-group/FairUnsupervisedRepresentations","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"f276005f2b7ebb39","entry":"get_random_batch","repo":"Nellaker-group/FairUnsupervisedRepresentations","repo_kind":"official","path":"scripts/AdversarialTraining.py","file_url":"https://github.com/Nellaker-group/FairUnsupervisedRepresentations/blob/HEAD/scripts/AdversarialTraining.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f276005f2b7ebb39"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}