Autoencoders

 

What is an autoencoder?

Autoencoders are neural networks that you can use to compress and reconstruct data.

An autoencoder is a type of artificial neural network that takes some kind of input data which can be images, vectors, audio or whatever, and it first compresses the original input data into a lower dimension and then uses this lower dimensional representation of the data to recreate the original input!

Autoencoder model architecture

An autoencoder consists of three major components as illustrated above:

1. An encoder which is a module that compresses the input data into an encoded representation that is several times smaller than the input data
2. The Bottleneck or Code which contains the compressed representations
3. And the decoder module that decompresses the bottleneck representation and tries to reconstruct the data back from its encoded form.

Applications of autoencoder models

Anomaly Detection

Anomaly detection is a perfect example, where we train the model on normal instances so that if we feed it any outliers, they will be detected easily. In this case, we can use the reconstruction loss as a metric to detect outliers.

Image Denoising

We already talked about the concept of training autoencoders where the input and outputs are identical and the task is to reproduce the input as closely as possible.

Image Denoising Autoencoder is like giving a blurry photo to an AI, which not only clears up the blur but also learns to enhance images by focusing on essential details.

Image Segmentation

Image segmentation is another example where autoencoders are used to take image inputs preserving spatial representation and aim to output semantic segmented counterparts of the image. This technique is used in self-driving cars to segment different objects in their environment.