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Feature extraction via projection on a large family of random filters. Works especially well if your weights have an orthogonal initialization (so you don't lose to much information in the process).
I was going to add a tweet but it didn't go through. What's interesting here is not that a random network would work, it's that training this random network does not improve its performance. It's a sign there's a fundamental flaw with how we think about the setup.
If random projections work at all, it should be very easy to tune these random projections to get a better network. No reason to believe the random weights were a local minimum.
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