Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments - Bai, Xiao; Wang, Xiang; Liu, Xianglong; Liu, Qiang; Song, Jingkuan; Sebe, Nicu; Kim, Been (Pattern recognition, 2021)
•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. Deep learning has recently achieved great success in many visual recognition tasks. However, the deep neural networks (DNNs) are often perceived as black-boxes, making their decision less understandable to humans and prohibiting their usage in safety-critical applications. This guest editorial introduces the thirty papers accepted for the Special Issue on Explainable Deep Learning for Efficient and Robust Pattern Recognition. They are grouped into three main categories: explainable deep learning methods, efficient deep learning via model compression and acceleration, as well as robustness and stability in deep learning. For each of the three topics, a survey of the representative works and latest developments is presented, followed by the brief introduction of the accepted papers belonging to this topic. The special issue should be of high relevance to the reader interested in explainable deep learning methods for efficient and robust pattern recognition applications and it helps promoting the future research directions in this field.