denoising
A common approach to self-supervised learning in which: 1.
Plain English Explanation
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.
How is it used?
Practitioners refer to denoising when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.