A review of some techniques for inclusion of domain-knowledge into deep neural networks

A review of some techniques for inclusion of domain-knowledge into deep neural networks

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Abstract We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks.The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding Trousers data using human-machine collaboration.In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form.This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks.The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed.

In each category, we describe techniques that have been shown to yield significant changes in the Plein air - Femme - Chaussures - Bottes performance of deep neural networks.