{"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/variational-autoencoder-with-arbitrary","title":"Variational Autoencoder with Arbitrary Conditioning","arxiv_id":"1806.02382","date":"2018-06-06","proceeding":"ICLR 2019 5","authors":["Oleg Ivanov","Michael Figurnov","Dmitry Vetrov"],"abstract":"We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in \"one shot\". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.","url_abs":"https://arxiv.org/abs/1806.02382v3","url_pdf":"https://arxiv.org/pdf/1806.02382v3.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":"variational-autoencoder-with-arbitrary","repo_url":"https://github.com/tigvarts/ucm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"variational-autoencoder-with-arbitrary","repo_url":"https://github.com/tigvarts/vaeac","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"variational-autoencoder-with-arbitrary","repo_url":"https://github.com/azraelzhor/tf2-VAEAC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"imputation","task_name":"Imputation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02382"}},"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/tigvarts/ucm","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/azraelzhor/tf2-VAEAC","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tigvarts/vaeac","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":7},"by_repo_kind":{"official":{"samples":7,"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":0,"samples":[{"code_sha256_prefix":"ce53b3b51ff21c74","entry":"categorical_parse_params_column","repo":"tigvarts/ucm","repo_kind":"official","path":"prob_utils.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/prob_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ce53b3b51ff21c74"}},{"code_sha256_prefix":"f195c200bfc3a313","entry":"compute_normalization","repo":"tigvarts/ucm","repo_kind":"official","path":"datasets.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/datasets.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f195c200bfc3a313"}},{"code_sha256_prefix":"6db2d0c299759c56","entry":"extend_batch","repo":"tigvarts/ucm","repo_kind":"official","path":"train_utils.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/train_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6db2d0c299759c56"}},{"code_sha256_prefix":"629c1fe4bfc34dc4","entry":"extend_batch_tuple","repo":"tigvarts/ucm","repo_kind":"official","path":"train_utils.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/train_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"629c1fe4bfc34dc4"}},{"code_sha256_prefix":"ce09de21e4235012","entry":"get_validation_iwae","repo":"tigvarts/ucm","repo_kind":"official","path":"train_utils.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/train_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ce09de21e4235012"}},{"code_sha256_prefix":"8cf9a01573b92861","entry":"normal_parse_params","repo":"tigvarts/ucm","repo_kind":"official","path":"prob_utils.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/prob_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8cf9a01573b92861"}},{"code_sha256_prefix":"bb98b06acff24a8c","entry":"optimizer","repo":"tigvarts/ucm","repo_kind":"official","path":"celeba_model/model.py","file_url":"https://github.com/tigvarts/ucm/blob/HEAD/celeba_model/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bb98b06acff24a8c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}