While I was reading papers about continual learning, I found that many researchers use
permutated MNIST to evaluate their approach.
I understand what it is but it is not clear to me why they use it?
What I understand is that they were trying to introduce noise (by applying a random permutation to the image) but the permutated images are very noisy which cannot be recognised even by a human.
Applying blur, rotation, or some distortion are understandable but why permuting the pixel?
PS. an example of the paper I mentioned: Three scenarios for continual learning