Data Augmentation
A technique used to artificially expand the size of a training dataset by applying random transformations like rotations or flips to existing data examples.
Think of It Like This
Like a basketball player practicing trick shots from different angles to prepare for any unexpected situation during a real game.
By generating modified copies of original data, models become more robust and less prone to memorizing the exact inputs. This is especially common in computer vision where images can be cropped, recolored, or blurred. These transformations help the neural network generalize better to unseen real-world variations.