  The low-rank tensor approximation is very promising for the compression of
deep neural networks. We propose a new simple and efficient iterative approach,
which alternates low-rank factorization with a smart rank selection and
fine-tuning. We demonstrate the efficiency of our method comparing to
non-iterative ones. Our approach improves the compression rate while
maintaining the accuracy for a variety of tasks.
