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

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This article introduces the success of deep learning in visual recognition tasks, but mentions the black-box problem, the difficulty of understanding decisions, and restrictions on safety-critical applications. It also outlines the three main categories of explainable deep learning methods, efficient deep learning via model compression and acceleration, and robustness and stability in deep learning, presenting representative works and latest developments.