An autoencoder is a type of artificial neural network used for unsupervised learning and dimensionality reduction. It is primarily designed to encode and decode data, with the main objective of learning a compact and efficient representation of the input data.

Autoencoder architecture

An autoencoder consists of two main parts: an encoder and a decoder. Its primary objective is to learn a compact representation (encoding) of the input data in a lower-dimensional space and then reconstruct the original data from this reduced representation.

How can autoencoder help to clean data

Autoencoders are trained to capture the underlying structure and patterns in data. When applied to noisy or corrupted data, they can help denoise the data by learning to retain essential features while filtering out noise. Such an auto encoder is called denoising auto encoder.

How can we create train dataset for denoising autoencoder

1. Obtain Clean Data: Start with a dataset containing clean, uncorrupted data that you want to denoise. This dataset should consist of samples representing the type of data you want to process, such as images, audio, or numerical data;
2. Introduce Noise: Determine the type of noise you want to add to the clean data. Adjust noise level: Set the intensity or magnitude of the noise. This parameter controls how much noise is added to the clean data;
3. Generate Noisy Data: For each sample in the clean dataset, apply the chosen noise type with the specified level to create noisy versions of the samples. The noisy data will serve as the input to the denoising autoencoder.

It's important to keep the clean data samples intact, as they will be used as the target output during training.