  Deep learning models, though having achieved great success in many different
fields over the past years, are usually data hungry, fail to perform well on
unseen samples, and lack of interpretability. Various prior knowledge often
exists in the target domain and their use can alleviate the deficiencies with
deep learning. To better mimic the behavior of human brains, different advanced
methods have been proposed to identify domain knowledge and integrate it into
deep models for data-efficient, generalizable, and interpretable deep learning,
which we refer to as knowledge-augmented deep learning (KADL). In this survey,
we define the concept of KADL, and introduce its three major tasks, i.e.,
knowledge identification, knowledge representation, and knowledge integration.
Different from existing surveys that are focused on a specific type of
knowledge, we provide a broad and complete taxonomy of domain knowledge and its
representations. Based on our taxonomy, we provide a systematic review of
existing techniques, different from existing works that survey integration
approaches agnostic to taxonomy of knowledge. This survey subsumes existing
works and offers a bird's-eye view of research in the general area of
knowledge-augmented deep learning. The thorough and critical reviews of
numerous papers help not only understand current progresses but also identify
future directions for the research on knowledge-augmented deep learning.
