  Compression has emerged as one of the essential deep learning research
topics, especially for the edge devices that have limited computation power and
storage capacity. Among the main compression techniques, low-rank compression
via matrix factorization has been known to have two problems. First, an
extensive tuning is required. Second, the resulting compression performance is
typically not impressive. In this work, we propose a low-rank compression
method that utilizes a modified beam-search for an automatic rank selection and
a modified stable rank for a compression-friendly training. The resulting BSR
(Beam-search and Stable Rank) algorithm requires only a single hyperparameter
to be tuned for the desired compression ratio. The performance of BSR in terms
of accuracy and compression ratio trade-off curve turns out to be superior to
the previously known low-rank compression methods. Furthermore, BSR can perform
on par with or better than the state-of-the-art structured pruning methods. As
with pruning, BSR can be easily combined with quantization for an additional
compression.
