3,159 research outputs found
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
Sharing deep generative representation for perceived image reconstruction from human brain activity
Decoding human brain activities via functional magnetic resonance imaging
(fMRI) has gained increasing attention in recent years. While encouraging
results have been reported in brain states classification tasks, reconstructing
the details of human visual experience still remains difficult. Two main
challenges that hinder the development of effective models are the perplexing
fMRI measurement noise and the high dimensionality of limited data instances.
Existing methods generally suffer from one or both of these issues and yield
dissatisfactory results. In this paper, we tackle this problem by casting the
reconstruction of visual stimulus as the Bayesian inference of missing view in
a multiview latent variable model. Sharing a common latent representation, our
joint generative model of external stimulus and brain response is not only
"deep" in extracting nonlinear features from visual images, but also powerful
in capturing correlations among voxel activities of fMRI recordings. The
nonlinearity and deep structure endow our model with strong representation
ability, while the correlations of voxel activities are critical for
suppressing noise and improving prediction. We devise an efficient variational
Bayesian method to infer the latent variables and the model parameters. To
further improve the reconstruction accuracy, the latent representations of
testing instances are enforced to be close to that of their neighbours from the
training set via posterior regularization. Experiments on three fMRI recording
datasets demonstrate that our approach can more accurately reconstruct visual
stimuli
Neurogenesis Deep Learning
Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference
on Neural Networks (IJCNN 2017
- …