Convolutional Autoencoder

Convolutional autoencoders (CAEs) are deep learning models used for unsupervised feature learning and dimensionality reduction, primarily aiming to reconstruct input data (e.g., images, videos, sensor data) from a compressed representation in a latent space. Current research focuses on enhancing CAE performance through architectural innovations like incorporating attention mechanisms, physics-informed loss functions, and hybrid models combining CAEs with other algorithms (e.g., recurrent networks, clustering methods). These advancements are driving applications in diverse fields, including anomaly detection in various domains (video surveillance, power systems, industrial processes), data compression, and improved performance in downstream tasks such as semantic segmentation and image classification.