Training Accurate Binary Neural Networks from Scratch
Binary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state of the art results on standard benchmark datasets. Further, we analyze how full precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state of the art even further. Our source code can be found online: https:/github.com/hpi xnor/BMXNet v2
2019 IEEE International Conference on Image Processing (ICIP)