  Deep neural networks have made significant progress in the field of computer
vision. Recent studies have shown that depth, width and shortcut connections of
neural network architectures play a crucial role in their performance. One of
the most advanced neural network architectures, DenseNet, has achieved
excellent convergence rates through dense connections. However, it still has
obvious shortcomings in the usage of amount of memory. In this paper, we
introduce a new type of pruning tool, threshold, which refers to the principle
of the threshold voltage in MOSFET. This work employs this method to connect
blocks of different depths in different ways to reduce the usage of memory. It
is denoted as ThresholdNet. We evaluate ThresholdNet and other different
networks on datasets of CIFAR10. Experiments show that HarDNet is twice as fast
as DenseNet, and on this basis, ThresholdNet is 10% faster and 10% lower error
rate than HarDNet.
