A computer vision expert makes many new training images by flipping, rotating, or zooming the original pictures. What big benefit does this give to the model?

The big benefit of making many new training images by flipping, rotating, or zooming original pictures, a technique known as data augmentation, is significantly improved model generalization. Generalization refers to a model's ability to perform accurately on new, unseen data that it was not explicitly trained on. By creating these varied versions of existing images, the training dataset, which is the collection of examples used to teach the model, becomes larger and more diverse ....