{"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/failing-conceptually-concept-based","title":"Failing Conceptually: Concept-Based Explanations of Dataset Shift","arxiv_id":"2104.08952","date":"2021-04-18","proceeding":null,"authors":["Maleakhi A. Wijaya","Dmitry Kazhdan","Botty Dimanov","Mateja Jamnik"],"abstract":"Despite their remarkable performance on a wide range of visual tasks, machine learning technologies often succumb to data distribution shifts. Consequently, a range of recent work explores techniques for detecting these shifts. Unfortunately, current techniques offer no explanations about what triggers the detection of shifts, thus limiting their utility to provide actionable insights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a novel explainable shift detection method. CBSD provides explanations by identifying and ranking the degree to which high-level human-understandable concepts are affected by shifts. Using two case studies (dSprites and 3dshapes), we demonstrate how CBSD can accurately detect underlying concepts that are affected by shifts and achieve higher detection accuracy compared to state-of-the-art shift detection methods.","url_abs":"https://arxiv.org/abs/2104.08952v2","url_pdf":"https://arxiv.org/pdf/2104.08952v2.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":"failing-conceptually-concept-based","repo_url":"https://github.com/maleakhiw/explaining-dataset-shifts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.08952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08952"}},"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/maleakhiw/explaining-dataset-shifts","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"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":2,"samples":[{"code_sha256_prefix":"c78acf848fcd0f5a","entry":"apply_adversarial_shift","repo":"maleakhiw/explaining-dataset-shifts","repo_kind":"official","path":"scripts/shift_applicator.py","file_url":"https://github.com/maleakhiw/explaining-dataset-shifts/blob/HEAD/scripts/shift_applicator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c78acf848fcd0f5a"}},{"code_sha256_prefix":"2ec461f202c20297","entry":"apply_gaussian_shift","repo":"maleakhiw/explaining-dataset-shifts","repo_kind":"official","path":"scripts/shift_applicator.py","file_url":"https://github.com/maleakhiw/explaining-dataset-shifts/blob/HEAD/scripts/shift_applicator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2ec461f202c20297"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}