Efficiency Analysis of Kolmogorov-Arnold Networks for Visual Data Processing

In the field of artificial neural networks, the use of multilayer perceptrons (MLPs) has long been a well-established methodology. Recently, the theory of Kolmogorov–Arnold Networks (KANs) has emerged as a potential alternative to multilayer perceptrons, inspired by the Kolmogorov–Arnold representation theorem. It has been demonstrated that solutions based on the Kolmogorov–Arnold Network (KAN) can achieve better efficiency than those based on the multilayer perceptron (MLP) for certain problems. In this work, we investigate how the new theory can be applied to a special image classification task when some adversarial attack method is applied. The aim of the research is to explore the potential of the theory to answer the question of its applicability to complex tasks of practical importance.