A mixed-scale dense convolutional neural network for image analysis
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
We report that deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. Lastly, we compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE
- Grant/Contract Number:
- AC02-05CH11231; AC03-76SF00098
- OSTI ID:
- 1414877
- Alternate ID(s):
- OSTI ID: 1485062
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Vol. 115, Issue 2; ISSN 0027-8424
- Publisher:
- National Academy of Sciences, Washington, DC (United States)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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