In this paper, we investigate lossy compression of deep neural networks
(DNNs) by weight quantization and lossless source coding for memory-efficient
deployment. Whereas the previous work addressed non-universal scalar
quantization and entropy coding of DNN weights, we for the first time introduce
universal DNN compression by universal vector quantization and universal source
coding. In particular, we examine universal randomized lattice quantization of
DNNs, which randomizes DNN weights by uniform random dithering before lattice
quantization and can perform near-optimally on any source without relying on
knowledge of its probability distribution. Moreover, we present a method of
fine-tuning vector quantized DNNs to recover the performance loss after
quantization. Our experimental results show that the proposed universal DNN
compression scheme compresses the 32-layer ResNet (trained on CIFAR-10) and the
AlexNet (trained on ImageNet) with compression ratios of $47.1$ and $42.5$,
respectively.

本文研究深度神经网络的权重量化和无损源编码的有损压缩以实现内存有效部署，通过引入通用向量量化和通用源编码，实现了通用的深度神经网络压缩，并尝试运用通用随机格量化方法来随机化神经网络权重，证明该方法在压缩 32 层的 ResNet 和 AlexNet 时具有较高的压缩比和较低的失真率。