Uncertainty-Aware Data Reconstruction in Autoencoders

The autoencoder represents an important Artificial Neural Network architecture designed to learn data representations in an unsupervised manner. Its structure, consisting of an encoder and a decoder, allows information to be compressed into a reduced-dimensional latent space and subsequently reconstructed. This process is crucial in many applications, such as dimensionality reduction, data compression, and noise removal. In addition, the autoencoder allows meaningful features to be extracted from the raw data, facilitating tasks such as image analysis and anomaly detection. The importance of this technique lies in its ability to reduce computational complexity and preserve essential information. However, autoencoders, used to compress and reconstruct signals, can exhibit significant variations in reconstruction quality, especially in the presence of noise or anomalies. Therefore, reconstructing the uncertainty band of the input signal to the autoencoder allows for a more accurate assessment of the quality of the reconstructed signal. This paper presents a methodology based on the law of propagation of uncertainty for reconstructing the input uncertainty band to increase the performance of an autoencoder.