{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115621"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115621","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards open world semi supervised detection","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Allabadi, Garvita"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Adve, Vikram"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Open world","Semi supervised learning","Object Detection"],"languages":["en","eng"],"rights":["Copyright 2022 Garvita Allabadi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115621","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Adve, Vikram"]},{"key":"dc:creator","label":"Author","values":["Allabadi, Garvita"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-29"]},{"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":["Open world","Semi supervised learning","Object Detection"]}]},{"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 2022 Garvita Allabadi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115621"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Garvita Allabadi, accepted the attached license on 2022-04-28 at 19:01.","The student, Garvita Allabadi, submitted this Thesis for approval on 2022-04-28 at 19:07.","This Thesis was approved for publication on 2022-04-29 at 08:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17989 on 2022-11-11 at 12:12:14","Traditional object detection networks work with large amounts of labeled data and under the assumption of a closed set, such that the test data only contains instances of classes already seen in the training set. These assumptions are challenged when deploying these methods in the wild. In this work we introduce Open World Semi Supervised Object Detection (OWSSD), a semi supervised learning framework that works in the open world setup. OWSSD effectively captures the novelty of unseen data compared to seen data and updates the detection framework to discover new classes on the fly."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards open world semi supervised detection"]}]}],"canonical_facts":{"dc:contributor":["Adve, Vikram"],"dc:creator":["Allabadi, Garvita"],"dc:date":["2022-05","2022-04-29"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Garvita Allabadi, accepted the attached license on 2022-04-28 at 19:01.","The student, Garvita Allabadi, submitted this Thesis for approval on 2022-04-28 at 19:07.","This Thesis was approved for publication on 2022-04-29 at 08:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17989 on 2022-11-11 at 12:12:14","Traditional object detection networks work with large amounts of labeled data and under the assumption of a closed set, such that the test data only contains instances of classes already seen in the training set. These assumptions are challenged when deploying these methods in the wild. In this work we introduce Open World Semi Supervised Object Detection (OWSSD), a semi supervised learning framework that works in the open world setup. OWSSD effectively captures the novelty of unseen data compared to seen data and updates the detection framework to discover new classes on the fly."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115621"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Garvita Allabadi"],"dc:subject":["Open world","Semi supervised learning","Object Detection"],"dc:title":["Towards open world semi supervised detection"],"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":"2026-07-22T22:24:54Z"}