"Dilated cnn"의 두 판 사이의 차이
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| (같은 사용자의 중간 판 4개는 보이지 않습니다) | |||
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| − | ==[https://arxiv.org/pdf/1511.07122.pdf Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015).]== | + | == [https://arxiv.org/pdf/1511.07122.pdf Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015).] == |
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| + | *dense prediction : "The goal is to compute a discrete or continuous label for each pixel in the image.” | ||
| + | **good example is semantic segmentation | ||
| + | ***multi-scale contextual reasoning? (He et al., 2004; Galleguillos & Belongie, 2010). | ||
| + | **ref. [https://arxiv.org/abs/1611.09288v2 Sercu, Tom, and Vaibhava Goel. "Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition." arXiv preprint arXiv:1611.09288 (2016).] | ||
| + | ** (almost) prerequisite : [https://arxiv.org/abs/1411.4038 Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.] | ||
| + | *'''The familiar discrete convolution is simply the 1-dilated convolution. ''' | ||
| + | *ref. [http://www.inference.vc/dilated-convolutions-and-kronecker-factorisation/ Dilated Convolutions and Kronecker Factored Convolutions] ★ | ||
| + | **[https://en.wikipedia.org/wiki/Kronecker_product Kronecker product (wikipedia)] | ||
| + | ** this ref is from [https://www.reddit.com/r/MachineLearning/comments/52drsq/what_is_dilated_convolution/ reddit]. Another links are there. | ||
| − | ==etc== | + | == etc == |
| − | * CRF : [https://arxiv.org/abs/1412.7062 Chen, Liang-Chieh, et al. "Semantic image segmentation with deep convolutional nets and fully connected crfs." arXiv preprint arXiv:1412.7062 (2014).] | + | |
| + | *CRF : [https://arxiv.org/abs/1412.7062 Chen, Liang-Chieh, et al. "Semantic image segmentation with deep convolutional nets and fully connected crfs." arXiv preprint arXiv:1412.7062 (2014).] | ||
2017년 3월 27일 (월) 23:55 기준 최신판
Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015).
- dense prediction : "The goal is to compute a discrete or continuous label for each pixel in the image.”
- good example is semantic segmentation
- multi-scale contextual reasoning? (He et al., 2004; Galleguillos & Belongie, 2010).
- ref. Sercu, Tom, and Vaibhava Goel. "Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition." arXiv preprint arXiv:1611.09288 (2016).
- (almost) prerequisite : Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
- good example is semantic segmentation
- The familiar discrete convolution is simply the 1-dilated convolution.
- ref. Dilated Convolutions and Kronecker Factored Convolutions ★
- Kronecker product (wikipedia)
- this ref is from reddit. Another links are there.