Le passage à l'échelle des analyses statiques de réseaux de neurones à l'aide de formes affines
Year: 2024
Type: dissertation
Abstract: Affine forms, which are an extension of interval arithmetic, have been successfully used to assess the robustness of neural networks as well as to explain their decisions. They are well-suited to this application domain since they capture affine relations between variables and since neural networks mostly perform affine computations. However, a drawback is that affine forms are time and memory consuming, making the verification of industrial-size neural network difficult or impossible. In this thesis, we introduce two new approaches to overcome these limitations. Affine compressed, merged serval noise symbols into a single noise symbol in a way that ensures the soundness of the computations. Block-wise noising simulates real-world situations in which particular portions of an image are disrupted by inserting non-zero noise symbols only inside a given section of the image. Using this methods, it is possible to assess neural networks resilience to these disturbances while preserving scalability and accuracy. Both methods have been implemented in a tool named, NNAFF. The experimental results have demonstrated the effectiveness of these methods, striking a balance between precision and execution time.
Author: Soualah, Asma
Institution: Université de Perpignan
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7001605585
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