https://www.selleckchem.com/products/hs-10296.html
This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a ``long-tail'' pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection sch
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