Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
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Abstract (arXiv)
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices. The sequential inputs can be perturbed in various ways, e.g., streaming images can be deformed due to robot motion or imperfect camera lens. Using the notion of the Voronoi diagram and Lipschitz properties of stable RNNs, we provide a thorough analysis and characterize the maximum allowable perturbations while guaranteeing the full accuracy of the classification task. We illustrate and validate our theoretical results using a map dataset with clouds as well as the MNIST dataset.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 10 Mar 2022.
Builds on
12 of 33 references.
References · 12
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.