It’s more of a regularization method. It can generate a lot of “additional” data that is mostly known to have the same label, which can prevent the model from o

It’s more of a regularization method. It can generate a lot of “additional” data that is mostly known to have the same label, which can prevent the model from overfitting to the 1 instance you actually have. It’s most successful with images - where it’s really easy to generate significant modifications that are definitely still semantically the same. (Crop, translate, blur, desaturate, …) How many true labels would you need to learn to be invariant to all that?