  Convolutional neural networks have achieved astonishing results in different
application areas. Various methods that allow us to use these models on mobile
and embedded devices have been proposed. Especially binary neural networks are
a promising approach for devices with low computational power. However,
training accurate binary models from scratch remains a challenge. Previous work
often uses prior knowledge from full-precision models and complex training
strategies. In our work, we focus on increasing the performance of binary
neural networks without such prior knowledge and a much simpler training
strategy. In our experiments we show that we are able to achieve
state-of-the-art results on standard benchmark datasets. Further, to the best
of our knowledge, we are the first to successfully adopt a network architecture
with dense connections for binary networks, which lets us improve the
state-of-the-art even further.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning