Singular values-driven automated filter pruning

In this paper, we present SLIMING (Singular vaLues-drIven autoMated filter prunING), an automated filter pruning method that uses singular values to formalize the pruning process as an optimization problem over filter tensors. Recognizing that this original formulation poses a combinatorial challenge, we propose to replace it with a two-step process that consistently uses singular values in each phase: (i) determining the pruning configuration, which specifies the number of filters to retain in each layer, and (ii) selecting the filters themselves. We show that this approach ensures the preservation of the filters' multidimensional structure throughout the pruning process. For each of these steps, we propose a straightforward algorithm to solve them. To validate each part of our approach, we performed a numerical simulation on an overparameterized synthetic toy example. Additionally, we conducted extensive simulations across eight architectures, four benchmark datasets, and four vision tasks, validating the efficacy of our framework. Our code is available for research purposes at sliming-ai.github.io.