{"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/generative-probabilistic-novelty-detection","title":"Generative Probabilistic Novelty Detection with Adversarial Autoencoders","arxiv_id":"1807.02588","date":"2018-07-06","proceeding":"NeurIPS 2018 12","authors":["Stanislav Pidhorskyi","Ranya Almohsen","Donald A. Adjeroh","Gianfranco Doretto"],"abstract":"Novelty detection is the problem of identifying whether a new data point is\nconsidered to be an inlier or an outlier. We assume that training data is\navailable to describe only the inlier distribution. Recent approaches primarily\nleverage deep encoder-decoder network architectures to compute a reconstruction\nerror that is used to either compute a novelty score or to train a one-class\nclassifier. While we too leverage a novel network of that kind, we take a\nprobabilistic approach and effectively compute how likely is that a sample was\ngenerated by the inlier distribution. We achieve this with two main\ncontributions. First, we make the computation of the novelty probability\nfeasible because we linearize the parameterized manifold capturing the\nunderlying structure of the inlier distribution, and show how the probability\nfactorizes and can be computed with respect to local coordinates of the\nmanifold tangent space. Second, we improved the training of the autoencoder\nnetwork. An extensive set of results show that the approach achieves\nstate-of-the-art results on several benchmark datasets.","url_abs":"http://arxiv.org/abs/1807.02588v2","url_pdf":"http://arxiv.org/pdf/1807.02588v2.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":"generative-probabilistic-novelty-detection","repo_url":"https://github.com/podgorskiy/GPND","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"novelty-detection","task_name":"Novelty Detection"},{"task_slug":"one-class-classifier","task_name":"One-class classifier"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02588"}},"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/podgorskiy/GPND","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":"c04fc6760dc7d267","entry":"r_pdf","repo":"podgorskiy/GPND","repo_kind":"official","path":"novelty_detector.py","file_url":"https://github.com/podgorskiy/GPND/blob/HEAD/novelty_detector.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c04fc6760dc7d267"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}