Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Highlights：

• A detailed survey of explainable deep learning for efficient and robust pattern recognition is represented.

• Explainable methods for deep neural networks, including visualization and uncertainty estimation, are categorized and presented.

• Model compression and acceleration methods for efficient deep learning are reviewed.

• Two major topics related to robust deep learning, adversarial robustness and stability in training neural networks, are covered.

• The accepted papers for the special issue on explainable deep learning for efficient and robust pattern recognition show the recent advances and promote further researches.