Application specific performance measure optimization using deep learning

dc.contributor.authorRahman, Md Atiqur
dc.contributor.examiningcommitteeLeung, Carson (Computer Science) Hu, Pingzhao (Biochemistry and Medical Genetics)en_US
dc.contributor.supervisorWang, Yang (Computer Science)en_US
dc.date.accessioned2016-09-19T16:58:02Z
dc.date.available2016-09-19T16:58:02Z
dc.date.issued2016en_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractIn this thesis, we address the action retrieval and the object category segmentation problems by directly optimizing application specific performance measures using deep learning. Most deep learning methods are designed to optimize simple loss functions (e.g., cross-entropy or hamming loss). These loss functions are suitable for applications where the performance of the application is measured by overall accuracy. But for many applications, the overall accuracy is not an appropriate performance measure. For example, applications like action retrieval often use the area under the Receiver Operating Characteristic curve (ROC curve) to measure the performance of a retrieval algorithm. Likewise, in object category segmentation from images, the intersection-over-union (IoU) is the standard performance measure. In this thesis, we propose approaches to directly optimize these complex performance measures in deep learning framework.en_US
dc.description.noteOctober 2016en_US
dc.identifier.citationRahman, M.A., and Wang, Y. Learning neural networks with ranking-based losses for action retrieval. In 13th conference on Computer and Robot Vision (CRV), 2016.en_US
dc.identifier.urihttp://hdl.handle.net/1993/31812
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsopen accessen_US
dc.subjectDeep learningen_US
dc.subjectAction retrievalen_US
dc.subjectObject category segmentationen_US
dc.subjectDirectly optimizing ROC-areaen_US
dc.subjectDirectly optimizing intersection-over-unionen_US
dc.titleApplication specific performance measure optimization using deep learningen_US
dc.typemaster thesisen_US
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