An autoencoder is a type of neural network with the condition that the output layer has the same dimension as that of the input layer. In other words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder is also known as a replicator neural network since it duplicates data from the input to the output in an unsupervised way.
By sending the input through the network, the autoencoders rebuild each dimension of the input. It may appear simple to use a neural network to replicate an input, however, the size of the input is reduced during the replication process, resulting in a smaller representation. In comparison to the input and output layers, the middle layers of the neural network have fewer units. As a result, the reduced representation of the input is stored in the middle layers. This reduced representation of the input is used to recreate the output.
Following are the different layers in the architecture of autoencoders :
Encoder: An encoder is a fully connected, feedforward neural network that compresses the input image into a latent space representation and encodes it as a compressed representation in a lower dimension. The deformed representation of the original image is the compressed image.
Code: The reduced representation of the input that is supplied into the decoder is stored in this section of the network.
Decoder: Like the encoder, the decoder is a feedforward network with a structure identical to the encoder. This network is in charge of reassembling the input from the code to its original dimensions.
As we can see in the above image, the input is compressed in the encoder, then stored in the Code, and then the original input is decompressed from the code by the decoder. The autoencoder's principal goal is to provide an output that is identical to the input.
