%0 Generic %1 rastegari2016xnornet %A Rastegari, Mohammad %A Ordonez, Vicente %A Redmon, Joseph %A Farhadi, Ali %D 2016 %K %T XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks %U http://arxiv.org/abs/1603.05279 %X We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.

@misc{rastegari2016xnornet, abstract = {We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.}, added-at = {2020-11-04T05:58:55.000+0100}, author = {Rastegari, Mohammad and Ordonez, Vicente and Redmon, Joseph and Farhadi, Ali}, biburl = {https://www.bibsonomy.org/bibtex/268e3c9192f8f124f1fdf9c20e717218f/vcus}, interhash = {65b5f00c865ae8821169825192bb5e6d}, intrahash = {68e3c9192f8f124f1fdf9c20e717218f}, keywords = {}, note = {cite arxiv:1603.05279}, timestamp = {2020-11-04T05:58:55.000+0100}, title = {XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks}, url = {http://arxiv.org/abs/1603.05279}, year = 2016 }