Transcription of Densely Connected Convolutional Networks
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Densely Connected Convolutional Networks
Densely Connected Convolutional NetworksGao Huang Cornell Liu Tsinghua van der MaatenFacebook AI Q. WeinbergerCornell work has shown that Convolutional Networks canbe substantially deeper, more accurate, and efficient to trainif they contain shorter connections between layers close tothe input and those close to the output. In this paper, weembrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layerto every other layer in a feed-forward fashion. Whereastraditional Convolutional Networks withLlayers haveLconnections one between each layer and its subsequentlayer our network hasL(L+1)2direct connections. Foreach layer, the feature -maps of all preceding layers areused as inputs, and its own feature -maps are used as inputsinto all subsequent layers. DenseNets have several com-pelling advantages: they alleviate the vanishing-gradientproblem, strengthen feature propagation, encourage fea-ture reuse, and substantially reduce the number of parame-ters.
cially, in contrast to ResNets, we never combine features through summation before they are passed into a layer; in-stead, we combine features by concatenating them. Hence, the ℓth layer has ℓ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps arepassedontoallL−ℓsubsequentlayers ...
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