  We present a memory and computation efficient ternary weight networks (TWNs)
- with weights constrained to +1, 0 and -1. The Euclidian distance between full
(float or double) precision weights and the ternary weights along with a
scaling factor is minimized in training stage. Besides, a threshold-based
ternary function is optimized to get an approximated solution which can be fast
and easily computed. TWNs have shown better expressive abilities than binary
precision counterparts. Meanwhile, TWNs achieve up to 16$\times$ model
compression rate and need fewer multiplications compared with the float32
precision counterparts. Extensive experiments on MNIST, CIFAR-10, and ImageNet
datasets show that the TWNs achieve much better result than the
Binary-Weight-Networks (BWNs) and the classification performance on MNIST and
CIFAR-10 is very close to the full precision networks. We also verify our
method on object detection task and show that TWNs significantly outperforms
BWN by more than 10\% mAP on PASCAL VOC dataset. The pytorch version of source
code is available at: https://github.com/Thinklab-SJTU/twns.
