This is quite common, it's often called data augmentation. For example, most CNNs aren't invariant to skew, distortions, rotations, or even zoom level. So to

This is quite common, it's often called data augmentation. For example, most CNNs aren't invariant to skew, distortions, rotations, or even zoom level. So to train a neural net to recognize both 8x8 pixel birds and 10x10 pixel birds, you need to add images of both zoom levels. Of course, this is a weakness, and there is a lot of research to try to rectify this, like Hinton's capsule networks. For things like adding noise, in some cases it is used as regularization to make the model robust to noise, in others, such as GANs, models are trained to learn the difference between the generatively created images that are higher entropy from the true images, to refine the model. But as the sibling noted, and you mention, the underlying data is somewhat the same, so yes, you do need a lot of diversity... but in practice these techniques can be helpful.