Understanding Autoencoders

What is the architecture of an autoencoder?

In practice, an auto-encoder consists of two parts. The first part is the encoder. The encoder’s goal is to condense the initially available data (image, text, audio, etc.) by extracting a features vector that characterizes the initial information. The vector resulting from the encoder is much smaller than the initial vector.

architecture of an autoencoder
Architecture of an autoencoder

What’s the latent space?

Now that we are familiar with encoder and decoder, let’s present the third main piece of an autoencoder: the latent space.

How do we train an autoencoder?

To train an auto-encoder, we feed it with input data that will be encoded, and then decoded. During the training, we expect the model’s output to be as close as possible to the input.

What are the applications of an autoencoder?

As I mentioned in the introduction, autoencoders have a lot of applications. Some of them are presented in this section.

Using autoencoders to generate images

Before GANs became the standards for image and art generation, autoencoders were used. To do this, we only have to use the decoder to decode vectors from the latent space that are not present in our dataset initially.

Latent space interpolation
Latent space interpolation

Autoencoders for image denoising

Autoencoders can also be used for image denoising.



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Ilyes Talbi

Ilyes Talbi


HEY! I am Ilyes. Freelance computer vision engineer and french bloger. I will help you to discover the world of AI :)