{"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/expert-gate-lifelong-learning-with-a-network","title":"Expert Gate: Lifelong Learning with a Network of Experts","arxiv_id":"1611.06194","date":"2016-11-18","proceeding":"CVPR 2017 7","authors":["Rahaf Aljundi","Punarjay Chakravarty","Tinne Tuytelaars"],"abstract":"In this paper we introduce a model of lifelong learning, based on a Network\nof Experts. New tasks / experts are learned and added to the model\nsequentially, building on what was learned before. To ensure scalability of\nthis process,data from previous tasks cannot be stored and hence is not\navailable when learning a new task. A critical issue in such context, not\naddressed in the literature so far, relates to the decision which expert to\ndeploy at test time. We introduce a set of gating autoencoders that learn a\nrepresentation for the task at hand, and, at test time, automatically forward\nthe test sample to the relevant expert. This also brings memory efficiency as\nonly one expert network has to be loaded into memory at any given time.\nFurther, the autoencoders inherently capture the relatedness of one task to\nanother, based on which the most relevant prior model to be used for training a\nnew expert, with finetuning or learning without-forgetting, can be selected. We\nevaluate our method on image classification and video prediction problems.","url_abs":"http://arxiv.org/abs/1611.06194v2","url_pdf":"http://arxiv.org/pdf/1611.06194v2.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":"expert-gate-lifelong-learning-with-a-network","repo_url":"https://github.com/wannabeOG/ExpertNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"expert-gate-lifelong-learning-with-a-network","repo_url":"https://github.com/ContinualAI/avalanche","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"lifelong-learning","task_name":"Lifelong learning"},{"task_slug":"video-prediction","task_name":"Video Prediction"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.06194","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}