Denoising Autoencoders

A Denoising Autoencoder is a type of neural network designed to improve data quality. It works by intentionally adding noise or distortions to input data, then training the model to reconstruct the original, clean version. This process encourages the network to learn the most important features of the data, making it robust against imperfections. In essence, it helps uncover underlying patterns by teaching the AI to "clean up" messy or incomplete information, which can be useful in tasks like image restoration, noise reduction, or feature extraction in various applications.