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While deep learning has pushed the boundaries in various machine learningtasks, the current models are still far away from replicating many functionsthat a normal human brain can do. Explicit memorization based deep architecturehave been recently proposed with the objective to understand and predictbetter. In this work, we design a system that involves a primary learner and anadjacent representational memory bank which is organized using a comparativelearner. This spatially forked deep architecture with a structured memory cansimultaneously predict and reason about the nature of an input, which may evenbelong to a category never seen in the training data, by relating it with thememorized past representations at the higher layers. Characterizing images ofunseen object classes in both synthetic and real world datasets is used as anexample to showcase the operational success of the proposed framework.

Structured Memory based Deep Model to Detect as well as Characterize Novel Inputs