Autoencoder

What is an autoencoder?

An autoencoder sets a artificial neural network This is used to learn extensive coding. This is used to learn extensive coding. The autoencoder extracts important features and learns to use the most essential information from data sets. In the process, data records can be transferred from a higher dimension to a lower one. Relevant data as well as information is not lost in the process.

What are the layers?

Here we highlight 3 different types of layers that play an important role in autoencoder.

Input layer

For example, when recognising faces, existing neurons can represent the individual pixels of a photo or image.

Smaller layers

These form the encoding in the first place.

Output layer

In this layer, each neuron is given the same attention and importance as the neurons within the input layer above.

Decoder

A decoder reconstructs the input from spatial representations that are latent. This becomes apparent with the help of the decoding function, which is declared with r=g(h).

Areas of application

When it comes to data projection, autodecoders can provide valuable services. However, they are not the best solution for compressing images. Encoders of this kind are trained for specific areas and data sets. This means that encoders from this area are better suited for data compression. For image data, other compression techniques should be used, for example JPEG or JPG.

It should be mentioned that as much data as possible should be preserved during coding and encoding; encoders who work automatically are always trained to do this. The new representation of data usually receives new properties. Each encoder fulfils certain purposes and tasks in order to safely achieve desired goals. There are four different types of autoencoders:

  • Multilayer Autoencoder
  • Regularised autoencoder
  • Vanilla autoencoder
  • Convolutional Autoencoder

Training

Autoencoders can be taught many properties and modes of operation. Frequently, variants of backpropagation take place. GG methods and gradient methods can also be trained effectively here. However, problems often arise with regard to the trainability of the neural networks in connection with layers that are hidden.

If errors do occur, they are classified as minor or insignificant over time, as an average calculation is created from the data of the respective training. It is similar to grades in school. If at the beginning of the school year you get a grade of B in a performance assessment and a grade of F in a later performance assessment, then the average is a grade of F.

The F represents the fault (blemish). The longer the school year lasts and the more positive grades are added, the less important the grade F from the beginning of the school year becomes in relation to the final grade point average. This is largely compensated for, so to speak. In autoencoding, pretraining takes place to prevent the formation of errors and to optimise processes in a targeted manner. The aim is to achieve an adequate approximation, so that a backprogagation is possible, which is used as a final fine-tuning.

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