Self Teaching Autoencoder

The article introduces the Self Teaching Autoencoder (STAE), a neural network architecture that learns to compress and reconstruct data without requiring labeled training data or a separate pre-training phase. It combines autoencoding with self-supervised learning principles, allowing the model to teach itself effective representations directly from raw input data.