Threshold Coverage refers to the proportion of neurons in a neural network that exceeds a specified activation value during testing. It helps assess the performance and robustness of neural networks under various conditions.
Key Points:
- Performance Evaluation: Indicates how well the network performs under specific conditions.
- Model Robustness: Helps identify areas where the model may fail or underperform.
- Optimisation Guidance: Provides insights for improving model architecture or training methods.
Understanding threshold coverage aids in refining neural network models for better accuracy and reliability.