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Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval

Permanent lenke
https://hdl.handle.net/10037/33195
DOI
https://doi.org/10.1109/ICASSP49357.2023.10094987
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article.pdf (5.377Mb)
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Dato
2023-05-05
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Agarwal, Rohit; Das, Gyanendra; Aggarwal, Saksham; Horsch, Ludwig Alexander; Prasad, Dilip Kumar
Sammendrag
Image 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.
Forlag
IEEE
Sitering
Agarwal 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. 2023
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  • Artikler, rapporter og annet (informatikk) [477]
Copyright 2023 The Author(s)

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