Denoising Autoencoder

Denoising autoencoders (DAEs) are neural networks designed to reconstruct clean data from noisy inputs, primarily aiming to improve data quality and extract meaningful features. Current research focuses on applying DAEs to diverse signal processing tasks, often integrating them with other architectures like U-Nets and Transformers, or employing them within larger frameworks for tasks such as anomaly detection and super-resolution. This versatility makes DAEs valuable tools across various fields, enhancing the accuracy and robustness of applications ranging from medical image analysis and radar signal processing to speech emotion recognition and building energy modeling.