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Sure but...45% accuracy is not exactly good. You can get close to 88% with a linear classifier. You can get 95% with nearest-neighbor/L2 distance. No deep learning necessary. But if you want more than 99% without losing your computational shirt, go with ConvNets.
Thanks! That's true
I would not recommend anyone use this classifier in seriousness
I was surprised it is working this well at all and better than nearest-neighbor on pixel sums.
At best, it's a simple proof-of-concept for information-theoretic approaches
You can get 45% accuracy on binarized MNIST using class-wise compression and counting bits 
No 
