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Image retrieval has garnered growing interest in recent times. The currentapproaches are either supervised or self-supervised. These methods do notexploit the benefits of hybrid learning using both supervision andself-supervision. We present a novel Master Assistant Buddy Network (MABNet)for image retrieval which incorporates both learning mechanisms. MABNetconsists of master and assistant blocks, both learning independently throughsupervision and collectively via self-supervision. The master guides theassistant by providing its knowledge base as a reference for self-supervisionand the assistant reports its knowledge back to the master by weight transfer.We perform extensive experiments on public datasets with and withoutpost-processing.

MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval