Stacked Robust Autoencoder for Classification

Porosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation

The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised ...

Experiments on classification of electroencephalography (EEG) signals in imagination of direction using Stacked Autoencoder

This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction measured by a portable EEG headset. In the authors’ previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE) for the clas...

Learning Stereo Features with Stacked Autoencoders

Single-layer stacked autoencoders have been shown to be successful in training artificial neurons with receptive fields that are similar to those found in the V1 cortex, but on monocular data. In this project we investigate extending a single-layer stacked autoencoder network to learn receptive fields on stereo data, and evaluate them with respect to their effectiveness as features for object c...

[Article] EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder