"Dilated cnn"의 두 판 사이의 차이
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| (같은 사용자의 중간 판 2개는 보이지 않습니다) | |||
| 5번째 줄: | 5번째 줄: | ||
***multi-scale contextual reasoning? (He et al., 2004; Galleguillos & Belongie, 2010). | ***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).] | **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).] | ||
| − | *'''The familiar discrete convolution is simply the 1-dilated convolution. '' | + | ** (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] ★ | *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)] | **[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.