  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.
