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

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 (MABNet) 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 Oxford, Paris and their revisited datasets with and without post-processing.