Autoencoder Neural Networks: A Performance Study Based On Image Reconstruction, Recognition And Compression

 AUTOENCODER NEURAL NETWORKS: A Performance Study Based on Image Reconstruction, Recognition and Compression PDF Text fb2 book

Autoencoders are feedforward neural networks which can have more than one hidden layer. These networks attempt to reconstruct the input data at the output layer. Since the size of the hidden layer in the autoencoders is smaller than the size of the input data, the dimensionality of input data is reduced to a smaller-dimensional code space at the hidden layer. However, training a multilayer autoenc...

Paperback: 96 pages
Publisher: LAP Lambert Academic Publishing (August 24, 2009)
Language: English
ISBN-10: 9783838309460
ISBN-13: 978-3838309460
ASIN: 3838309464
Product Dimensions: 5.9 x 0.2 x 8.7 inches
Amazon Rank: 3120546
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ious. This is due to the fact that the weights at deep hidden layers are hardly optimized. The research work has focused on the characteristics, training and performance evaluation of autoencoders. The concepts of stacking and Restricted Boltzmann Machine have also been discussed in detail. Two datasets, namely ORL face dataset and MNIST handwritten digit dataset have been employed in these experiments. The performances of the autoencoders have also been compared with that of PCA. It has been shown that the autoencoders can also be used for image compression. The compression efficiency has been studied using DDSM dataset (mammogram dataset). Since image patches were used for training, it was possible to compress and decompress mammograms of different sizes.