Autoencoders are a type of neural network which generates an “n-layer” coding of the given input and attempts to reconstruct the input using the code generated. The Autoencoder architecture ...

Abstract: The presented work proposes an effective approach for extracting abstract characteristics from image data using the autoencoder-based models. Since simple autoencoders do not deliver the ...

Abstract: We present a novel stacked autoencoder framework for feature extraction to improve classification of hyperspectral image, leveraging graph regularization to address the shortcomings of ...

Hyperspectral images (HSIs) are actively used for landuse/land-cover classification. However ... In this paper, we propose an autoencoder (3D ResAE) that uses 3D convolutions and residual blocks to ...

This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first ...

Another approach to feature extraction is to use an unsupervised ... To select and extract features for text classification, we used a stacked autoencoder. Hidden layer activations were used ...