When a paper claims 0.1 top-5 on ImageNet with a feature extractor trained on 10% of the data w/o looking at labelspic.twitter.com/Qp5Nnp7pEb
Not that I think it's a bad idea. I've been trying to get stacked matrix-factorization-based conv feature extractors to work for years
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If claims are true it would be v exciting. But I cannot believe it at this point. Too outlandish especially in light of my own experience
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did anything from the paper stand out as potentially significant in comparison to your previous work?
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