Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
This work addresses robustness in RNNs for classification tasks, which is crucial for applications like robotics and image processing, but it is incremental as it builds on existing stability and Lipschitz analysis methods.
The authors tackled the problem of quantifying robustness bounds for stable recurrent neural networks (RNNs) under perturbed sequential inputs, such as deformed images from robot motion, by using Voronoi diagrams and Lipschitz properties to characterize maximum allowable perturbations while guaranteeing full classification accuracy, and validated their theoretical results on map and MNIST datasets.
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.