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View a PDF of the paper titled LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks, by Dongqing Zhang and 2 other authors
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks - microsoft/LQ-Nets
我们将通过我们的方法量化的网络称为"LQ-Nets"。 我们使用CIFAR-10和ImageNet数据集上的图像分类任务评估我们的LQ-Nets。 实验结果表明，它们在各种网络结构中表现非常好，如AlexNet，VGG-Net，GoogLeNet，ResNet和DenseNet ，大大超过了以前的量化方法。
For weight-only quantization, our LQ-Nets outperformed BWN, TWN, TTQ and DoReFa-Net by large margins. As for quantizing both weights and activations, our results are significantly better than DoReFa-Net and HWGQ when using very low bit-widths (1 bit for weights and few for activations).
This document provides a high-level introduction to LQ-Nets (Learned Quantization Networks), explaining the system's purpose, core concepts, and architecture. It serves as the entry point for understa
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks 07/26/2018 ∙ by Dongqing Zhang, et al.
Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation ...
LQ-Nets（Learned Quantization for Highly Accurate and Compact Deep Neural Networks）是由微软开发的一个开源项目，旨在通过学习量化技术来提高深度 神经网络 （DNN）的准确性和压缩率。
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks Dongqing Zhang , Jiaolong Yang , Dongqiangzi Ye , and Gang Hua
The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin.