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Modern deep learning models usually suffer high complexity in model size and computation when transplanted to resource constrained platforms. To this end, many works are dedicated to compressing deep neural networks. Adding group LASSO regularization is one of the most effective model compression methods since it generates structured sparse networks. We investigate the deep neural networks trained by group LASSO constraint and observe that even with strong sparsity regularization imposed, theredoi:10.24963/ijcai.2018/453 dblp:conf/ijcai/ZhuZL18 fatcat:nvkuvyjuwbfkveytejuurrg5hq