Autoencoder Model

Autoencoders are neural network models designed to learn efficient data representations by encoding input data into a lower-dimensional latent space and then reconstructing the original data from this compressed representation. Current research focuses on improving reconstruction quality, particularly through incorporating techniques like diffusion models and adversarial losses, and extending autoencoders to handle diverse data types, including images, audio, and multi-modal data, often using architectures such as masked autoencoders and transformers. This versatility makes autoencoders valuable tools across numerous fields, from image compression and anomaly detection to sound source localization and the prediction of treatment effects in healthcare.