  Concepts are the foundation of human deep learning, understanding, and
knowledge integration and transfer. We propose concept-oriented deep learning
(CODL) which extends (machine) deep learning with concept representations and
conceptual understanding capability. CODL addresses some of the major
limitations of deep learning: interpretability, transferability, contextual
adaptation, and requirement for lots of labeled training data. We discuss the
major aspects of CODL including concept graph, concept representations, concept
exemplars, and concept representation learning systems supporting incremental
and continual learning.
