  It has been proven that, compared to using 32-bit floating-point numbers in
the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with
low precision during inference, thereby saving memory space and power
consumption. However, quantizing networks is always accompanied by an accuracy
decrease. Here, we propose a method, double-stage Squeeze-and-Threshold
(double-stage ST). It uses the attention mechanism to quantize networks and
achieve state-of-art results. Using our method, the 3-bit model can achieve
accuracy that exceeds the accuracy of the full-precision baseline model. The
proposed double-stage ST activation quantization is easy to apply: inserting it
before the convolution.

Artificial Intelligence;Computer Vision and Pattern Recognition