{"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/hyper-process-model-a-zero-shot-learning","title":"Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis","arxiv_id":"1810.10330","date":"2018-10-16","proceeding":null,"authors":["Joao Reis","Gil Gonçalves"],"abstract":"Zero-shot learning (ZSL) can be defined by correctly solving a task where no\ntraining data is available, based on previous acquired knowledge from\ndifferent, but related tasks. So far, this area has mostly drawn the attention\nfrom computer vision community where a new unseen image needs to be correctly\nclassified, assuming the target class was not used in the training procedure.\nApart from image classification, only a couple of generic methods were proposed\nthat are applicable to both classification and regression. These learn the\nrelation among model coefficients so new ones can be predicted according to\nprovided conditions. So far, up to our knowledge, no methods exist that are\napplicable only to regression, and take advantage from such setting. Therefore,\nthe present work proposes a novel algorithm for regression problems that uses\ndata drawn from trained models, instead of model coefficients. In this case, a\nshape analyses on the data is performed to create a statistical shape model and\ngenerate new shapes to train new models. The proposed algorithm is tested in a\ntheoretical setting using the beta distribution where main problem to solve is\nto estimate a function that predicts curves, based on already learned\ndifferent, but related ones.","url_abs":"http://arxiv.org/abs/1810.10330v1","url_pdf":"http://arxiv.org/pdf/1810.10330v1.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":"hyper-process-model-a-zero-shot-learning","repo_url":"https://github.com/joaoreis-feup/hyper_process_model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hyper-process-model-a-zero-shot-learning","repo_url":"https://github.com/jpcreis/Hyper-Process-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}