Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.
Binarized Neural Networks on the ImageNet Classification Task
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abstract
We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET. We expect networks of better performance can be obtained by following our current strategies. We provide a detailed discussion and preliminary analysis on strategies used in the network training.
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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What does it mean to understand a neural network?
Simple training code produces complex neural networks, suggesting that brain learning rules may be easier to understand than mature brain properties and that neuroscience should shift focus accordingly.