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: this https URL

本文提出一种具有高效内存和计算的三进制权重网络（TWNs）方法，通过对浮点或双精度精度权重和三进制权重之间的欧几里德距离进行训练，实现了最小化计算开销。TWNs 的表达能力优于二进制精度的相应网络，并且在 MNIST、CIFAR-10 和 ImageNet 数据集上实现了比二进制网络更好的分类性能，同时在 PASCAL VOC 数据集上进行的物体检测任务中，相较于二进制网络，TWNs 的 mAP 表现也有了超过 10％的提升。