An autoencoder is a regular neural network, an unsupervised learning model that takes an input and produces the same input in the output layer. So, there is no associated label in the training data. Generally, an autoencoder consists of two parts:
- Encoder network
- Decoder network
It learns all the required features from unlabeled training data, which is known as lower dimensional feature representation. In the following figure, the input data (x) is passed through an encoder that produces a compressed representation of the input data. Mathematically, in the equation, z = h(x), z is a feature vector, and is usually a smaller dimension than x.
Then, we take these produced features from the input data and pass them through a decoder network to reconstruct the original data.
An encoder can be a fully connected neural network or a convolutional neural network (CNN). A decoder also uses the same kind of network as an encoder. Here, we've explained and implemented the encoder...
