Densely Connected Convolutional Networks

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. In this paper, we embrace this observation and introduce the Dense Convolutional N...

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Vydáno v:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 2261 - 2269
Hlavní autoři: Huang, Gao, Liu, Zhuang, Van Der Maaten, Laurens, Weinberger, Kilian Q.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2017
Témata:
Convolution
Convolutional codes
Network architecture
Neural networks
Road transportation
Training
ISSN:1063-6919, 1063-6919
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Shrnutí: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. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections-one between each layer and its subsequent layer-our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2017.243