New notes on using ML to generalize from small amounts of training data: attacking MNIST with just 10 training examples for each digit. 93.81% accuracy: http://cognitivemedium.com/rmnist_anneal_ensemble …
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No, with the same hyperparameters. (This is not uncommon.) The difference that arises is due to differences in initial weights. Using different convnets with different hps would certainly be worthwhile, but I haven't done it.
(The main focus, as you can probably tell from my post, was the annealing, not the use of the ensemble. I wanted to find the best possible hyper-parameters for a single net. )
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oh ya totally. It just happened that last night I was thinking of how to ensemble covnets and cuoldnt quite get my hands around the idea since as ive said originally I always thought you only avrg very different models.
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