What are autoencoders?

An autoencoder is a neural network that has three layers:

An input layer, a hidden layer which is also known as the encoding layer, and a decoding layer.

• This network is trained to reconstruct its inputs, which forces the hidden layer to try to learn good representations of the inputs.

• An autoencoder neural network is an unsupervised Machine-learning algorithm that applies backpropagation, setting the target values to be equal to the inputs.

• An autoencoder is trained to attempt to copy its input to its output.

• Internally, it has a hidden layer that describes a code used to represent the input.

Autoencoder Components:

Encoder:

• In this, the model learns how to reduce the input dimensions and compress the input data into an encoded representation.

Bottleneck:

• In this, the layer contains the compressed representation of the input data. This is the lowest possible dimension of the input data.

Decoder:

• In this, the model learns how to reconstruct the data from the encoded represented to be as close to the original inputs as possible.

Reconstruction Loss:

• This method measures how well the decoder is performing and how close the output is related to the original input.

Types of Autoencoders:

1. Denoising Auto Encoder (DAE)

2. Sparse Auto Encoder (SAE)

3. Variational Auto Encoder (VAE)

4. Contractive Auto Encoder (CAE)