{"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/190408486","title":"Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset","arxiv_id":"1904.08486","date":"2019-04-02","proceeding":"CVPR 2019 6","authors":["Martin Mundt","Sagnik Majumder","Sreenivas Murali","Panagiotis Panetsos","Visvanathan Ramesh"],"abstract":"Recognition of defects in concrete infrastructure, especially in bridges, is\na costly and time consuming crucial first step in the assessment of the\nstructural integrity. Large variation in appearance of the concrete material,\nchanging illumination and weather conditions, a variety of possible surface\nmarkings as well as the possibility for different types of defects to overlap,\nmake it a challenging real-world task. In this work we introduce the novel\nCOncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification\nof five commonly appearing concrete defects. We investigate and compare two\nreinforcement learning based meta-learning approaches, MetaQNN and efficient\nneural architecture search, to find suitable convolutional neural network\narchitectures for this challenging multi-class multi-target task. We show that\nlearned architectures have fewer overall parameters in addition to yielding\nbetter multi-target accuracy in comparison to popular neural architectures from\nthe literature evaluated in the context of our application.","url_abs":"http://arxiv.org/abs/1904.08486v1","url_pdf":"http://arxiv.org/pdf/1904.08486v1.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":"190408486","repo_url":"https://github.com/MrtnMndt/meta-learning-CODEBRIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"190408486","repo_url":"https://github.com/SAGNIKMJR/CODEBRIM_MetaQNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[{"slug":"codebrim","name":"CODEBRIM","full_name":"COncrete DEfect BRidge IMage Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}