Reservoir Computing for image classification

Abstract

Reservoir Computing (RC) is a machine learning framework designed to solve time-dependent tasks, but it remains less explored for time-independent tasks. In this work, we adapt RC for image classification by flattening input images into one-dimensional vectors and feeding the entire vector in a single step into an RC. We evaluate our approach on the MNIST and COIL-100 datasets, demonstrating that RC achieves competitive accuracy while requiring significantly shorter training times compared to traditional neural networks. Our experiments show that the difference between the worst and best performance across reservoir network densities is 1–3%. Increasing the reservoir size generally improves accuracy; for instance, increasing the reservoir from 100 to 1000 typically yields a 10–15% improvement, whereas increasing it further from 1000 to 2000 results in only a 3–5% gain. This work highlights RC as an alternative for image classification tasks on standard and moderately sized datasets.