Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163989
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCai, Qiongen_US
dc.date.accessioned2022-12-29T09:23:11Z-
dc.date.available2022-12-29T09:23:11Z-
dc.date.issued2022-
dc.identifier.citationCai, Q. (2022). Transfer learning based on different image retrieval models. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163989en_US
dc.identifier.urihttps://hdl.handle.net/10356/163989-
dc.description.abstractWith the rapid development of digital technology, image retrieval has been used in more and more applications; for example, commodity retrieval, scenic spot retrieval, etc. However, the difficulty of collecting different types of images varies from cases to cases. Some images are easy to collect and there are large amounts of data for model training. On the other hand, some images are difficult to come by and it is not easy to have enough data for model training. Under such situation, transfer learning can be applied to address partly the problem of insufficient training set. In this dissertation, we will use two datasets (the animal dataset and the Pokemon dataset) to perform transfer learning on three image retrieval models which build on lightweight convolution network, heavyweight convolution network, and U-Net, respectively. The mAP (mean average precision) is used as the evaluation indicator to explore the effect of transfer learning for these three models. Through experiments, we conclude that the effect of model-based transfer learning on image retrieval model based on lightweight convolutional neural network is more profound. Keywords: Auto-Encoder, Convolution Neural Network, U-Net, Transfer Learning, Image Retrieval.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleTransfer learning based on different image retrieval modelsen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorTan Yap Pengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailEYPTan@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
dissertation-CaiQiong-20221229.pdf
  Restricted Access
3.61 MBAdobe PDFView/Open

Page view(s)

139
Updated on Apr 16, 2024

Download(s)

1
Updated on Apr 16, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.