The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification

This paper evaluates the worst-case divergence between original neural networks and their convex relaxations, which are used in verification systems to improve performance at the cost of soundness. The study provides analytical upper and lower bounds for the error, demonstrating it grows exponentially with network depth and linearly with the input's radius.