Neuron Coverage

Neuron coverage, a metric assessing the extent to which different parts of a neural network are activated during testing, aims to improve the robustness and reliability of deep learning models. Current research focuses on developing more effective neuron coverage metrics, exploring their correlation with model performance and generalization, and applying them within various neuroevolutionary algorithms and testing frameworks, including those for natural language processing models. These efforts are driven by the need for more rigorous testing methodologies for deep learning models, particularly in safety-critical applications, and seek to move beyond simple neuron activation towards a more nuanced understanding of network behavior and its relationship to model accuracy and fault detection.