This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Towards open world semi supervised detection
Director of Research (if dissertation) or Advisor (if thesis)
Department of Study
Degree Granting Institution
University of Illinois at Urbana-Champaign
Semi supervised learning
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.