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 without requiring the costly collection of entirely new original data. This increased diversity exposes the model to a wider range of appearances of the same object or scene. For instance, if a model is being trained to identify a car, applying data augmentation by slightly rotating or zooming car images teaches the model that a car is still a car even if its orientation or size in the image changes. This process directly addresses and helps prevent overfitting. Overfitting occurs when a model learns the training data too well, essentially memorizing specific examples and noise rather than learning the fundamental, underlying patterns. An overfit model performs poorly on new, real-world data because it expects inputs to look exactly like the specific examples it saw during training. Data augmentation forces the model to learn more robust and universally applicable features by showing it the same object under different common transformations. This makes the model more reliable and effective when encountering new images in real-world scenarios, which naturally exhibit variations in orientation, size, and perspective.