Self-supervised learning with masking is based on selectively masking (covering) parts of the input data and the model learning to reconstruct this missing information.
In the field of natural language processing, masked language modeling (MLM) is a well-known example, where individual words in a sentence are randomly removed and replaced with a mask.
The model is trained to predict the masked words based on the remaining context, thereby learning deep language representations.
In the field of image processing, there are similar approaches where image regions are masked and the model learns to reconstruct or classify these pixels.
This method allows for the effective use of large amounts of unlabeled data and the learning of robust feature representations that can be used for many tasks.