Evaluation of Autoencoders: Training Using Original, Encoded and Decoded Images for Prediction
Keywords:
Autoencoder, encode, decode, dimensionality reduction, feature learning, neural network.Abstract
An autoencoder (AE) is a neural network that seeks to make the same contribution to production and performance by directly reducing the inputs to the latent domain map and reconstructing the outputs from that map. Today data processing and reduction of data view size are considered to be the two most effective autoencoder systems. With the right size and sparsity limits, autoencoders can learn more interesting data predictions than PCA or other basic techniques. In this paper, we implement different types of autoencoders and evaluate them. Original image or raw image is taken and is trained. Using this training information testing is being performed on image prediction. This is being measured based on accuracy as well as time. The model is also applied by giving input as an encoded image which is an intermediate result of autoencoder and giving input as a decoded image a final result of autoencoder. All the above three models are applied and results are compared by implementing different types of autoencoders.
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