Binary neural network
It saves storage and computation, and serves as a technique for deep models on resource-limited devices. Using binary values can bring up to 58 times speedup. Accuracy and information capacity of binary neural network can be manually controlled. Binary neural networks do not achieve the same accuracy as their full-precision counterparts, but improvements are being made to close this gap.
References[change | change source]
- Courbariaux, M.; Bengio, Y.; David, J.-P. (2015). "BinaryConnect: training deep neural networks with binary weights during propagation". NIPS. arXiv:1511.00363.
- Rastegari, M.; Ordonez, V.; Redmon, J.; Farhadi, A. (2016). "XNOR-Net: ImageNet classification using binary convolutional neural networks". ECCV. arXiv:1603.05279.
- Ignatov, D.; Ignatov, A. (2020). "Controlling information capacity of binary neural network". Pattern Recognition Letters. arXiv:2008.01438. doi:10.1016/j.patrec.2020.07.033.