Resilience and Self-Healing of Deep Convolutional Object Detectors

Faiz Ur Rahman, Bhavan Vasu, Andreas E. Savakis

Abstract: The enormous success and popularity of deep convolutional neural networks for object detection has prompted their deployment in various real world applications. However, their performance in the presence of hardware faults or damage that could occur in the field has not been studied. This paper explores the resiliency of three popular network architectures for object detection, AlexNet, VGG and ResNet50, when subjected to various degrees of failures. We introduce failures in a deep network by dropping a percentage of weights at each layer. We then assess the effects of these failures on classification performance. Finally, we determine the ability of the network to self-heal and recover its performance by retraining its healthy portions after partial damage. To our knowledge, this is the first time that this type of study has been conducted.