This paper tackles the challenging problem of output range estimation for Deep Neural Networks (DNNs), introducing a novel algorithm based on Simulated Annealing (SA). Our approach addresses the lack of local geometric information and high non-linearity in DNNs, making it versatile across various architectures, especially Residual Neural Networks (ResNets). We present a straightforward, implementation-friendly algorithm that avoids restrictive assumptions about network architecture. Through theoretical analysis and experimental evaluations, including tests on the Ackley function, we demonstrate our algorithm's effectiveness in navigating complex, non-convex surfaces and accurately estimating DNN output ranges. Futhermore, the Python codes of this experimental evaluation that support our results are available in our GitHub repository (https://github.com/Nicerova7/output-range-analysis-for-deep-neural-networks-with-simulated-annealing).

本文介绍了一种基于模拟退火算法的新算法，用于解决深度神经网络(DNNs)的输出范围估计问题，通过理论分析和实验评估，包括对Ackley函数的测试，展示了该算法在导航复杂非凸表面和准确估计DNN输出范围方面的有效性。

基于模拟退火过程的深度神经网络输出范围分析