Eltwise　layer　is　a　commonly　used　structure　in　the　multi-branch　deep　learning　network.　In　a　filter-wise　pruning　procedure,　due　to　the　specific　operation　of　the　eltwise　layer,　all　its　previous　convolutional　layers　should　vote　for　which　filters　by　index　should　be　pruned.　Since　only　an　intersection　of　the　voted　filters　is　pruned,　the　compression　rate　is　limited.　This　work　proposes　a　method　called　Directed-Weighting　Group　Lasso　(DWGL),　which　enforces　an　index-wise　incremental　(directed)　coefficient　on　the　filter-level　group　lasso　items,　so　that　the　low　index　filters　getting　high　activation　tend　to　be　kept　while　the　high　index　ones　tend　to　be　pruned.　When　using　DWGL,　much　fewer　filters　are　retained　during　the　voting　process　and　the　compression　rate　can　be　boosted.　The　paper　test　the　proposed　method　on　the　ResNet　series　networks.　On　CIFAR-10,　it　achieved　a　75.34%　compression　rate　on　ResNet-56　with　a　0.94%　error　increment,　and　a　52.06%　compression　rate　on　ResNet-20　with　a　0.72%　error　increment.　On　ImageNet,　it　achieved　a　53%　compression　rate　with　ResNet-50　with　a　0.6%　error　increment,　speeding　up　the　network　by　2.23　times.　Furthermore,　it　achieved　a　75%　compression　rate　on　ResNet-50　with　a　1.2%　error　increment,　speeding　up　the　network　by　4　times.　©　2019.　The　copyright　of　this　document　resides　with　its　authors.