  Image retrieval has garnered 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 (MABNet)
for image retrieval which incorporates both learning mechanisms. MABNet
consists of master and assistant blocks, 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 public datasets with and without
post-processing.
