  We tackle the problem of unsupervised visual descriptors compression, which
is a key ingredient of large-scale image retrieval systems. While the deep
learning machinery has benefited literally all computer vision pipelines, the
existing state-of-the-art compression methods employ shallow architectures, and
we aim to close this gap by our paper. In more detail, we introduce a DNN
architecture for the unsupervised compressed-domain retrieval, based on
multi-codebook quantization. The proposed architecture is designed to
incorporate both fast data encoding and efficient distances computation via
lookup tables. We demonstrate the exceptional advantage of our scheme over
existing quantization approaches on several datasets of visual descriptors via
outperforming the previous state-of-the-art by a large margin.
