Description: A denoising autoencoder is a type of neural network used to learn how to reconstruct a clean input from a corrupted version of it. This process involves introducing noise into the input data, allowing the model to learn to identify and remove unwanted disturbances. The architecture of a denoising autoencoder consists of two main parts: the encoder, which compresses the input into a lower-dimensional representation, and the decoder, which reconstructs the original input from this compressed representation. This type of autoencoder is particularly useful in data preprocessing tasks, where the goal is to improve the quality of inputs before they are used in machine learning models. Additionally, denoising autoencoders can capture relevant features of the data, making them valuable tools in dimensionality reduction and feature extraction. Their ability to learn robust representations from noisy data makes them applicable in various fields, such as computer vision, signal processing, and data enhancement, where data quality may be compromised by noise or distortion.
