Classical Autoencoders

Classical autoencoders are unsupervised machine learning models that learn compressed representations of data by encoding and decoding information through a narrow "bottleneck" layer. Current research focuses on improving their performance in various applications, including image generation (often hybridized with GANs or VAEs) and data denoising, exploring architectures like rank reduction autoencoders to enhance interpolation and address overfitting issues. These advancements are significant for diverse fields, enabling improved data analysis, feature extraction, and model calibration in areas such as traffic flow modeling and process optimization.