Auto Encoder

Autoencoders are neural networks designed to learn compressed representations of data by encoding input into a lower-dimensional latent space and then reconstructing the original input from this compressed representation. Current research focuses on improving autoencoder architectures for specific tasks, such as anomaly detection in various data types (e.g., time series, images, and graphs), and enhancing their efficiency through techniques like masked autoencoding and optimized projection methods. These advancements are impacting diverse fields, including seismic signal processing, speaker recognition, and medical image analysis, by enabling unsupervised learning, improved feature extraction, and more efficient data representation.