Masked Autoencoder

Masked autoencoders (MAEs) are a self-supervised learning technique that reconstructs incomplete data, forcing models to learn robust and generalizable representations. Current research focuses on applying MAEs to diverse data types, including images, time series (like ECG and EEG signals), point clouds, and even multimodal data combining vision and language, often incorporating transformer architectures for improved performance. This approach is proving highly effective in various applications, from improving medical image analysis and biosignal classification to enhancing action recognition and natural language understanding in resource-constrained domains, by leveraging pre-training on large unlabeled datasets to boost downstream task performance.