  While convolutional neural networks (CNNs) have become the de facto standard
for most image processing and computer vision applications, their deployment on
edge devices remains challenging. Tensor decomposition methods provide a means
of compressing CNNs to meet the wide range of device constraints by imposing
certain factorization structures on their convolution tensors. However, being
limited to the small set of factorization structures presented by
state-of-the-art decomposition approaches can lead to sub-optimal performance.
We propose SeKron, a novel tensor decomposition method that offers a wide
variety of factorization structures, using sequences of Kronecker products. By
recursively finding approximating Kronecker factors, we arrive at optimal
decompositions for each of the factorization structures. We show that SeKron is
a flexible decomposition that generalizes widely used methods, such as
Tensor-Train (TT), Tensor-Ring (TR), Canonical Polyadic (CP) and Tucker
decompositions. Crucially, we derive an efficient convolution projection
algorithm shared by all SeKron structures, leading to seamless compression of
CNN models. We validate SeKron for model compression on both high-level and
low-level computer vision tasks and find that it outperforms state-of-the-art
decomposition methods.
