Original paper
Fully Convolutional Networks for Semantic Segmentation
Volume: 39, Issue: 4, Pages: 640 - 651
Published: Apr 1, 2017
Abstract
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...
Paper Details
Title
Fully Convolutional Networks for Semantic Segmentation
Published Date
Apr 1, 2017
Volume
39
Issue
4
Pages
640 - 651
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