Sign Change Coverage measures how many neurons in a neural network activate with both positive and negative values during tests. This metric assesses the network’s sensitivity to input variations by examining how changes affect neuron activation states.
Key Features:
- Activation Analysis: Evaluates how neuron outputs change with different input values.
- Sensitivity Assessment: Helps identify which parts of the network respond significantly to input changes.
- Comprehensive Evaluation: Ensures that both positive and negative activations are considered in tests.
Sign change coverage is important for understanding neural network behaviour under varying conditions, contributing to improved model robustness.