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        "description": "Filter magnitude is a practical proxy, with layer sensitivity and pruning ratios empirically selected. Greedy multi-layer selection is not globally optimal and residual links impose structural constraints. Accuracy recovery and speed depend on retraining, architecture and implementation, not only nominal filter/FLOP reductions.",
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      "statement": "# Structured CNN filter pruning by weight magnitude\n\nRanked trained convolutional filters by absolute weight sums and removed corresponding feature maps and downstream kernels, producing smaller dense operations rather than isolated sparse weights.\n\n## Scientific scope\n\nFilter magnitude is a practical proxy, with layer sensitivity and pruning ratios empirically selected. Greedy multi-layer selection is not globally optimal and residual links impose structural constraints. Accuracy recovery and speed depend on retraining, architecture and implementation, not only nominal filter/FLOP reductions.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nHao Li; Asim Kadav; Igor Durdanovic; Hanan Samet; Hans Peter Graf\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Pruning Filters for Efficient ConvNets](https://arxiv.org/abs/1608.08710v1)\n",
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