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That's not quite how I understood it > Do the cutout in feature space, not the original input space. I think they do the cutout in the original input space, b
by lopuhin 5y ago
That's not quite how I understood it
> Do the cutout in feature space, not the original input space.
I think they do the cutout in the original input space, based on examples they show, e.g. they mask parts of text and grey out parts of an image.
> and make the network predict the missing part
I think predict the latent representation (as you say in (2)), but not of the missing part, but of the whole corrupted sample, and require it to be close to latent representation of the original input done by the teacher.
- l-lousy 5y agoSo they’re doing both masking for the inputs and knowledge distillation? Is it the combination of these two methods that’s novel?
- WithinReason 5y agoYou're right, they cut in the original space and average-pool the features before an L1/L2 loss