I’ve read that when increasing the size of your dataset via augmentations you should limit those to things that are realistic and could actually occur in a test set. However, someone I know often gets significantly improved performance in object detection problems by using drastic augmentations, warpings, and color effects that are very unrealistic.
I’m trying to understand why this might be and if there are any theoretical justifications that support doing these sorts of augmentations. If anyone has experience in this topic or papers to share please weigh in!