  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.
