Use Autoencoders to Denoise Data

Contributed by: Sofien Kaabar

Difficulty level: Easy

Data denoising refers to the process of removing/reducing noise or unwanted variations from time series data. The purpose of data denoising is to improve the quality and clarity of the underlying signal in the time series, making it easier to analyze, model, and extract meaningful information. 

A denoising autoencoder (DAE) denoises data by learning to remove noise or unwanted variations from corrupted input data. The fundamental idea behind DAEs is to train a neural network to map noisy or corrupted data back to its clean, noise-free representation. The way a DAE works can be summarized as follows:

1. The model deliberately introduces noise or corruption into the input data. This step is essential for teaching the network to recover the clean data. This step is referred to as corruption. Common types of corruption include adding Gaussian noise, dropping out random elements, or applying other forms of distortion.

2. The corrupted input data is then passed through an encoder neural network. The encoder’s role is to map the noisy input data to a lower-dimensional representation. The encoder learns to capture essential features and patterns in the data while filtering out the noise and other unwanted variations.

3. The encoded representation is passed to the decoder neural network. The decoder’s task is to reconstruct the clean or denoised version of the data from the encoding. The decoder’s architecture ...