{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120293"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120293","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"MACON: memory-augmented continual learning for open-world classification","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Lyu, Weijie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hoiem, Derek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Continual Learning","Open-world Recognition","Open-vocabulary Classification","Memory-augmented Neural Network"],"languages":["en","eng"],"rights":["Copyright 2023 Weijie Lyu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120293","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hoiem, Derek"]},{"key":"dc:creator","label":"Author","values":["Lyu, Weijie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-20"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Continual Learning","Open-world Recognition","Open-vocabulary Classification","Memory-augmented Neural Network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Weijie Lyu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Weijie Lyu, accepted the attached license on 2023-04-19 at 18:52.","The student, Weijie Lyu, submitted this Thesis for approval on 2023-04-19 at 21:35.","This Thesis was approved for publication on 2023-04-20 at 14:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19053 on 2023-09-01 at 17:09:04","Emerging concepts and rapidly changing environments necessitate AI models that can quickly adapt to new scenarios without losing their inherited capabilities. Large foundation models like CLIP offer a strong zero-shot learning baseline under an open-vocabulary classification scenario. However, their massive training data makes re-training or fine-tuning impractical without sacrificing zero-shot performance. We introduce Memory-Augmented CONtinual learning (MACON), a novel framework for addressing open-world continual learning challenges. The core idea is to augment foundation models, such as CLIP, with memory to provide context and flexibility for better decision-making and prevention of catastrophic forgetting. We propose various memory retrieval methods tailored to different continual learning scenarios. Our results demonstrate that MACON exhibits fast adaptation capabilities, minimal forgetting issues, and robust generalization abilities, making it suitable for a wide range of open-world applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["MACON: memory-augmented continual learning for open-world classification"]}]}],"canonical_facts":{"dc:contributor":["Hoiem, Derek"],"dc:creator":["Lyu, Weijie"],"dc:date":["2023-05","2023-04-20"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Weijie Lyu, accepted the attached license on 2023-04-19 at 18:52.","The student, Weijie Lyu, submitted this Thesis for approval on 2023-04-19 at 21:35.","This Thesis was approved for publication on 2023-04-20 at 14:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19053 on 2023-09-01 at 17:09:04","Emerging concepts and rapidly changing environments necessitate AI models that can quickly adapt to new scenarios without losing their inherited capabilities. Large foundation models like CLIP offer a strong zero-shot learning baseline under an open-vocabulary classification scenario. However, their massive training data makes re-training or fine-tuning impractical without sacrificing zero-shot performance. We introduce Memory-Augmented CONtinual learning (MACON), a novel framework for addressing open-world continual learning challenges. The core idea is to augment foundation models, such as CLIP, with memory to provide context and flexibility for better decision-making and prevention of catastrophic forgetting. We propose various memory retrieval methods tailored to different continual learning scenarios. Our results demonstrate that MACON exhibits fast adaptation capabilities, minimal forgetting issues, and robust generalization abilities, making it suitable for a wide range of open-world applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120293"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Weijie Lyu"],"dc:subject":["Continual Learning","Open-world Recognition","Open-vocabulary Classification","Memory-augmented Neural Network"],"dc:title":["MACON: memory-augmented continual learning for open-world classification"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}