  Network quantization is an effective solution to compress deep neural
networks for practical usage. Existing network quantization methods cannot
sufficiently exploit the depth information to generate low-bit compressed
network. In this paper, we propose two novel network quantization approaches,
single-level network quantization (SLQ) for high-bit quantization and
multi-level network quantization (MLQ) for extremely low-bit quantization
(ternary).We are the first to consider the network quantization from both width
and depth level. In the width level, parameters are divided into two parts: one
for quantization and the other for re-training to eliminate the quantization
loss. SLQ leverages the distribution of the parameters to improve the width
level. In the depth level, we introduce incremental layer compensation to
quantize layers iteratively which decreases the quantization loss in each
iteration. The proposed approaches are validated with extensive experiments
based on the state-of-the-art neural networks including AlexNet, VGG-16,
GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.
