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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 ascaling factor is minimized in training stage. Besides, a threshold-basedternary function is optimized to get an approximated solution which can be fastand easily computed. TWNs have shown better expressive abilities than binaryprecision counterparts. Meanwhile, TWNs achieve up to 16$\times$ modelcompression rate and need fewer multiplications compared with the float32precision counterparts. Extensive experiments on MNIST, CIFAR-10, and ImageNetdatasets show that the TWNs achieve much better result than theBinary-Weight-Networks (BWNs) and the classification performance on MNIST andCIFAR-10 is very close to the full precision networks. We also verify ourmethod on object detection task and show that TWNs significantly outperformsBWN by more than 10\% mAP on PASCAL VOC dataset. The pytorch version of sourcecode is available at: https://github.com/Thinklab-SJTU/twns.

Ternary Weight Networks