Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning

Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The autoencoder SSL framework learns by reconstructing its own input after information loss through a bottleneck or noise injection.