[recursivecaveat]

Everything old is new again: in the Alexnet paper that kicked off the deep learning wave in 2012, they describe horizontally flipping every image as a cheap form of data augmentation. Though now that we expect models to actually read text that seems potentially counter-productive. Rotations are similar, in that you'd hope it would learn heuristics such as that the sky is almost always at the top.

[latency-guy2]

At least from when I was still doing this kind of work, look angle/platform angle scatterer signal (radar) mattered more than rotation, but rotation was a simple way to get quite a bit more samples. It never stopped being relevant :)

[bonoboTP]

That's called data augmentation. It was common alredy before AlexNet. And it never stopped being common, it's still commonly done.