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DenseNets have several compelling\nadvantages: they alleviate the vanishing-gradient problem, strengthen feature\npropagation, encourage feature reuse, and substantially reduce the number of\nparameters. We evaluate our proposed architecture on four highly competitive\nobject recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet).\nDenseNets obtain significant improvements over the state-of-the-art on most of\nthem, whilst requiring less computation to achieve high performance. Code and\npre-trained models are available at https://github.com/liuzhuang13/DenseNet .","short_abstract":"Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.","url_abs":"http://arxiv.org/abs/1608.06993v5","url_pdf":"http://arxiv.org/pdf/1608.06993v5.pdf","authors":"[\"Gao Huang\", \"Zhuang Liu\", \"Laurens van der Maaten\", \"Kilian Q. 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