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dc.contributor.authorAgarwal, Rohit
dc.contributor.authorDas, Gyanendra
dc.contributor.authorAggarwal, Saksham
dc.contributor.authorHorsch, Ludwig Alexander
dc.contributor.authorPrasad, Dilip Kumar
dc.date.accessioned2024-03-19T14:36:29Z
dc.date.available2024-03-19T14:36:29Z
dc.date.issued2023-05-05
dc.description.abstractImage retrieval has garnered a growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MAB-Net) for image retrieval which incorporates both the learning mechanisms. MABNet consists of master and assistant block, both learning independently through supervision and collectively via self-supervision. The master guides the assistant by providing its knowledge base as a reference for self-supervision and the assistant reports its knowledge back to the master by weight transfer. We perform extensive experiments on the public datasets with and without post-processing.en_US
dc.identifier.citationAgarwal R, Das, Aggarwal, Horsch A, Prasad DK. Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 2023en_US
dc.identifier.cristinIDFRIDAID 2185313
dc.identifier.doi10.1109/ICASSP49357.2023.10094987
dc.identifier.issn1520-6149
dc.identifier.issn2379-190X
dc.identifier.urihttps://hdl.handle.net/10037/33195
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleMabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrievalen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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