Denoising is a learning or preprocessing approach in which noise is added to data or present in data and the model learns to recover the cleaner signal. It is used in self-supervised learning, masked language modeling, diffusion systems, and data restoration tasks.
A common approach to self-supervised learning in which: 1. Noise is artificially added to the dataset. 2. The model tries to remove the noise. Denoising enables learning from unlabeled examples. The original dataset serves as the target or label and the noisy data as the input. Some masked language models use denoising as follows: 1. Noise is artificially added to an unlabeled sentence by masking some of the tokens. 2. The model tries to predict the original tokens.