Improving deep learning with prior knowledge and cognitive models: A
  survey on enhancing explainability, adversarial robustness and zero-shot
  learning
Fuseinin Mumuni Alhassan Mumuni
We review current and emerging knowledge-informed and brain-inspired
cognitive systems for realizing adversarial defenses, eXplainable Artificial
Intelligence (XAI), and zero-shot or few-short learning. Data-driven deep
learning models have achieved remarkable performance and demonstrated
capabilities surpassing human experts in many applications. Yet, their
inability to exploit domain knowledge leads to serious performance limitations
in practical applications. In particular, deep learning systems are exposed to
adversarial attacks, which can trick them into making glaringly incorrect
decisions. Moreover, complex data-driven models typically lack interpretability
or explainability, i.e., their decisions cannot be understood by human
subjects. Furthermore, models are usually trained on standard datasets with a
closed-world assumption. Hence, they struggle to generalize to unseen cases
during inference in practical open-world environments, thus, raising the zero-
or few-shot generalization problem. Although many conventional solutions exist,
explicit domain knowledge, brain-inspired neural network and cognitive
architectures offer powerful new dimensions towards alleviating these problems.
Prior knowledge is represented in appropriate forms and incorporated in deep
learning frameworks to improve performance. Brain-inspired cognition methods
use computational models that mimic the human mind to enhance intelligent
behavior in artificial agents and autonomous robots. Ultimately, these models
achieve better explainability, higher adversarial robustness and data-efficient
learning, and can, in turn, provide insights for cognitive science and
neuroscience-that is, to deepen human understanding on how the brain works in
general, and how it handles these problems.