{"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-prototyping-encoder-one-shot","title":"Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images","arxiv_id":"1904.08482","date":"2019-04-17","proceeding":"CVPR 2019 6","authors":["Junsik Kim","Tae-Hyun Oh","Seokju Lee","Fei Pan","In So Kweon"],"abstract":"In daily life, graphic symbols, such as traffic signs and brand logos, are\nubiquitously utilized around us due to its intuitive expression beyond language\nboundary. We tackle an open-set graphic symbol recognition problem by one-shot\nclassification with prototypical images as a single training example for each\nnovel class. We take an approach to learn a generalizable embedding space for\nnovel tasks. We propose a new approach called variational prototyping-encoder\n(VPE) that learns the image translation task from real-world input images to\ntheir corresponding prototypical images as a meta-task. As a result, VPE learns\nimage similarity as well as prototypical concepts which differs from widely\nused metric learning based approaches. Our experiments with diverse datasets\ndemonstrate that the proposed VPE performs favorably against competing metric\nlearning based one-shot methods. Also, our qualitative analyses show that our\nmeta-task induces an effective embedding space suitable for unseen data\nrepresentation.","url_abs":"http://arxiv.org/abs/1904.08482v1","url_pdf":"http://arxiv.org/pdf/1904.08482v1.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-prototyping-encoder-one-shot","repo_url":"https://github.com/mibastro/VPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.08482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08482"}},"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/mibastro/VPE","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"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":"69ad3254ae3d792d","entry":"convNoutput","repo":"mibastro/VPE","repo_kind":"official","path":"code/models/vaeIdsiaStn.py","file_url":"https://github.com/mibastro/VPE/blob/HEAD/code/models/vaeIdsiaStn.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":"69ad3254ae3d792d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}