Coupled Tensor Decomposition for Compact Network Representation

In this article, we introduce an approach called coupled filters decomposition, which builds on the key observation that redundancy exists among filters in a convolutional layer, meaning that similar filters can produce partially overlapping outputs. Leveraging this insight, we propose a joint decomposition of filters using coupled tensor decompositions, specifically coupled canonical polyadic decomposition (CPD), which enables the sharing of a common factor matrix across similar filters. This joint factorization not only reduces the number of parameters but also lowers computational complexity by eliminating redundant computations. To further improve efficiency, we first cluster the filters before decomposition. The grouping relies on a custom metric based on the subspace spanned by the shared-mode factor. Within each group, the coupling constraint is less restrictive. Extensive experiments across various architectures, datasets, and tasks validate the effectiveness of our method, demonstrating its competitive performance compared to state-of-the-art model compression techniques. Our code is available for research purposes at https://codec-ai.github.io/.