Convolutional Networks with Dense Connectivity
Tsinghua University · University of California, Berkeley · +2 more institutions
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. 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 [Formula: see text] 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…
Citation impact
- FWCI
- 31.19
- Percentile
- 100%
- References
- 71
Authors
5Topics & keywords
- Computer science
- Artificial intelligence
- Convolutional neural network
- Pattern recognition (psychology)
- Industry, innovation and infrastructure