On the application of reservoir computing networks for noisy image recognition

Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved

Feature enhancement with a Reservoir-based Denoising Auto Encoder

Recently, automatic speech recognition has advanced significantly by the introduction of deep neural networks for acoustic modeling. However, there is no clear evidence yet that this does not come at the price of less generalization to conditions that were not present during training. On the other hand, acoustic modeling with Reservoir Computing (RC) did not offer improved clean speech recognition but it leads to good robustness against noise and channel distortions. In this paper, the aim is to establish whether adding feature denoising in the front-end can further improve the robustness of an RC-based recognizer, and if so, whether one can devise an RC-based Denoising Auto Encoder that outperforms a traditional denoiser like the ETSI Advanced Front-End. In order to answer these questions, experiments are conducted on the Aurora-2 benchmark