We introduce Ternary Weight Networks (TWNs) -neural networks with weights constrained to +1, 0 and -1. The L2 distance between the full (float or double) precision weights and the ternary weights along with a scaling factor is minimized. With the optimization, the TWNs own high capacity of model expression that is good enough to approximate the Full Precision Weight Networks (FPWNs) counterpart. Besides, the TWNs achieve up to 16x or 32x model compression rate and own much fewer multiplications compared with the FPWNs. Compared with recently proposed Binary Precision Weight Networks (BPWNs), the TWNs own nearly 38x more power of expression in a 3×3 size filter, which is commonly used in most of the state-of-the-art CNN models like residual networks or VGG. Besides, the TWNs eliminate the singularity at zero and converge faster and more stablely at training time. Benchmarks on MNIST, CIFAR-10, and the large scale ImageNet dataset show that TWNs achieve state-of-the-art performance which is only slightly worse than the FPWNs counterpart but outperforms the analogous BPWNs.
Ternary Weight Network (TWN) REF constrains the weights to ternary values (−1, 0, 1) by referencing symmetric thresholds.
Ternary Weight Networks
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ArXiv
Mathematics Computer Science