Reviews: Efficient Neural Network Robustness Certification with General Activation Functions

Summary: This paper proposes a general framework CROWN to efficiently certify robustness of neural networks with general activation functions. CROWN adaptively bounds a given activation function with linear and quadratic functions, so it can tackle general activation functions including but not limited to the four popular choices: ReLU, tanh, sigmoid, and arctan. Experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed framework. Quality: We are glad to find a work which conducts the efficiently certifying of the non-trivial robustness for general activation functions in neural networks. It is also interesting that the proposed framework can flexibly select upper bounds and lower bounds which can reduce the approximation error.