{"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/meta-networks","title":"Meta Networks","arxiv_id":"1703.00837","date":"2017-03-02","proceeding":"ICML 2017 8","authors":["Tsendsuren Munkhdalai","Hong Yu"],"abstract":"Neural networks have been successfully applied in applications with a large\namount of labeled data. However, the task of rapid generalization on new\nconcepts with small training data while preserving performances on previously\nlearned ones still presents a significant challenge to neural network models.\nIn this work, we introduce a novel meta learning method, Meta Networks\n(MetaNet), that learns a meta-level knowledge across tasks and shifts its\ninductive biases via fast parameterization for rapid generalization. When\nevaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve\na near human-level performance and outperform the baseline approaches by up to\n6% accuracy. We demonstrate several appealing properties of MetaNet relating to\ngeneralization and continual learning.","url_abs":"http://arxiv.org/abs/1703.00837v2","url_pdf":"http://arxiv.org/pdf/1703.00837v2.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":"meta-networks","repo_url":"https://bitbucket.org/tsendeemts/metanet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.00837","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}