An Introduction to Autoencoders in Deep Learning

Understanding the principles behind autoencoders

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Autoencoders (AEs) are a type of neural network architecture that is able to find a compressed representation of the input data such as image, video, text, speech, etc.

Autoencoders fall under unsupervised learning algorithms as they learn the compressed representation of the data automatically from the input data without labels.

Practical applications of autoencoders

I decided to list down the following applications of autoencoders at the beginning of the article as this will motivate you to learn more about autoencoders!

• Transform noisy data into clean data using denoising autoencoders (denoising)
• Add color to grayscale images (image colonization) using automatic colonization autoencoders
• Increase the resolution of images to improve the details (super-resolution)
• Compress images to save memory (image compression)
• Reduce the dimensionality of data (dimensionality reduction)
• Extract the most important features of the input data (feature extraction)
• Generate new images with slight variations using…