HIGHLIGHTS
SUMMARY
Convolutional neural_networks (CNN), which have been applied to semantic segmentation, have unquestionably contributed to the recent rise in interest in this subject. The training time for large neural_networks still remains an issue, so smaller architectures are still required to reduce the time to deploy the CNN-based solution for some domains. Section 3 introduces and describes the proposed neural_network architectures. Previous Work on Semantic Image Segmentation VGG-16 is a neural_network architecture introduced by Simonyan and Zisserman. The authors suggested a fully convolutional neural_network (FCN) that utilizes small 3 × 3 convolutional filters. Each segment . . .
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