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An autoencoder (AE) is a specific kind of unsupervised artificial neural network that provides compression and other functionality in the field of machine learning. The specific use of the autoencoder is to use a feedforward approach to reconstitute an output from an input. The input is compressed and then sent to be decompressed as output, which is often similar to the original input. That is the nature of an autoencoder – that the similar inputs and outputs get measured and compared for execution results.
An autoencoder is also known as an autoassociator or diabolo network.
An autoencoder has three essential parts: an encoder, a code and a decoder. The original data goes into a coded result, and the subsequent layers of the network expand it into a finished output. One way to understand autoencoders is to take a look at a “denoising” autoencoder. The denoising autoencoder uses original inputs along with a noisy input, to refine the output and rebuild something representing the original set of inputs. Autoencoders are helpful in image processing, classification and other aspects of machine learning.
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