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Fully Convolutional Networks for Semantic Segmentation
[article]
2016
arXiv
pre-print
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application
arXiv:1605.06211v1
fatcat:gls74fuavrgsfaqpwgdx6ycjee