{"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/robust-and-accurate-compositional","title":"Robust and Accurate -- Compositional Architectures for Randomized Smoothing","arxiv_id":"2204.00487","date":"2022-04-01","proceeding":null,"authors":["Miklós Z. Horváth","Mark Niklas Müller","Marc Fischer","Martin Vechev"],"abstract":"Randomized Smoothing (RS) is considered the state-of-the-art approach to obtain certifiably robust models for challenging tasks. However, current RS approaches drastically decrease standard accuracy on unperturbed data, severely limiting their real-world utility. To address this limitation, we propose a compositional architecture, ACES, which certifiably decides on a per-sample basis whether to use a smoothed model yielding predictions with guarantees or a more accurate standard model without guarantees. This, in contrast to prior approaches, enables both high standard accuracies and significant provable robustness. On challenging tasks such as ImageNet, we obtain, e.g., $80.0\\%$ natural accuracy and $28.2\\%$ certifiable accuracy against $\\ell_2$ perturbations with $r=1.0$. We release our code and models at https://github.com/eth-sri/aces.","url_abs":"https://arxiv.org/abs/2204.00487v1","url_pdf":"https://arxiv.org/pdf/2204.00487v1.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":"robust-and-accurate-compositional","repo_url":"https://github.com/eth-sri/aces","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.00487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00487"}},"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":"deterministic:regex_extraction","url":"https://github.com/eth-sri/aces","reach":null}],"summary":{"ran_honours":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"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":3,"samples":[{"code_sha256_prefix":"ae0cdebd5b267ea9","entry":"get_data","repo":"eth-sri/aces","repo_kind":"official","path":"analyze_utils.py","file_url":"https://github.com/eth-sri/aces/blob/HEAD/analyze_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ae0cdebd5b267ea9"}},{"code_sha256_prefix":"6a2fd1b3c0b44765","entry":"get_acr","repo":"eth-sri/aces","repo_kind":"official","path":"analyze_utils.py","file_url":"https://github.com/eth-sri/aces/blob/HEAD/analyze_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6a2fd1b3c0b44765"}},{"code_sha256_prefix":"175f70004a53a2de","entry":"get_natural_accuracy","repo":"eth-sri/aces","repo_kind":"official","path":"analyze_utils.py","file_url":"https://github.com/eth-sri/aces/blob/HEAD/analyze_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"175f70004a53a2de"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}