Lessons from the Human Mind

Deep learning models have shown remarkable performance across various applications. However, their resilience to hardware faults, particularly the loss of neurons, is a critical aspect that needs to be addressed. In this paper, we propose a novel approach for improving the resilience of deep learning models. Through a carefully designed experiment, we evaluate the impact of neuron loss on these models in order to cultivate a deeper understanding of deep learning models' resilience and to identify methods for enhancing their reliability in the face of neuronal loss. This is done by investigating a new technique that focuses on pruning the most important neurons and then retraining the model to distribute the work among all the neurons. This technique along with dropout demonstrates great results that significantly enhanced resilience after neuron loss. This research bears significant importance, as deep learning models are increasingly applied across a wide range of domains, including computer vision, self-driving cars, and robotics. To ensure the dependability and stability of these systems, it is crucial to acknowledge their limitations and potential vulnerabilities, and to devise ways to strengthen their resilience. By examining the resilience of deep learning models concerning neuronal loss, we can gain valuable insights that will inform the design of more reliable artificial neural networks for real-world applications.