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Not sure where you're getting this from, but CNNs are definitely still state of the art on MNIST. Papers often cite outdated CNN numbers; in fact the best publi
by argonaut 10y ago
Not sure where you're getting this from, but CNNs are definitely still state of the art on MNIST. Papers often cite outdated CNN numbers; in fact the best published CNN accuracy numbers are probably lower than they could be - vanilla MNIST for supervised learning is a pretty useless benchmark for computer vision researchers now.
- Seanny123 10y agoMy bad, I should have been more specific. The Deep PCA paper shows superior performance not on baseline MNIST, but on the MNIST variations. I also agree that vanilla MNIST is pretty useless for Computer Vision researches and was trying (awkwardly) to support that idea by showing how these other non Deep Learning techniques performed equally well.
- argonaut 10y agoThe Deep PCA paper shows no such thing. They've cherry-picked bad CNN baselines. The red flag for me is that the "state of the art" methods they compare to achieve > 1% test error on vanilla MNIST, which is totally wrong since CNNs routinely achieve <0.5 % test error (even by the standards of 2014, when the paper was published).