Output Range Analysis for Deep Neural Networks based on Simulated Annealing Processes

• URL: http://arxiv.org/abs/2407.02700v1
• Date: Tue, 2 Jul 2024 22:47:40 GMT
• Title: Output Range Analysis for Deep Neural Networks based on Simulated Annealing Processes
• Authors: Helder Rojas, Nilton Rojas, Espinoza J. B., Luis Huamanchumo,
• Abstract summary: This paper introduces a novel approach to range estimation for Deep Neural Networks (DNNs) Our approach addresses the lack of local geometric information and high non-linearity in DNNs, making it versatile. We present a straightforward,friendly algorithm that avoids restrictive assumptions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-annealin g).

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