Autoencoder Based

Autoencoder-based methods are a powerful class of deep learning models used for dimensionality reduction, feature extraction, and data compression across diverse scientific domains. Current research focuses on applying autoencoders (including variations like variational autoencoders and masked autoencoders) to tasks such as anomaly detection, image and video compression, and the analysis of complex datasets in fields like astronomy, network traffic analysis, and materials science. These techniques offer significant advantages in handling high-dimensional data, improving computational efficiency, and enabling novel analyses that were previously intractable, impacting fields ranging from network security to medical image analysis.