Dilated cnn - 편집 역사

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Admin: 새 문서: [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).]

2017-03-23T12:58:11Z

새 문서: [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).]

새 문서

[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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 ***multi-scale contextual reasoning? (He et al., 2004; Galleguillos &amp; 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).]
 *'''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] ★

@@ 5번째 줄: / 5번째 줄: @@
 ***multi-scale contextual reasoning? (He et al., 2004; Galleguillos &amp; 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).]
-*'''The familiar discrete convolution is simply the 1-dilated convolution. ''’
+*'''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)]

@@ 8번째 줄: / 8번째 줄: @@
 *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)]
 == etc ==
 *CRF&nbsp;: [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).]

@@ 1번째 줄: / 1번째 줄: @@
-==[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).] ==
-==etc==
+*dense prediction&nbsp;: "The goal is to compute a discrete or continuous label for each pixel in the image.”
-* 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).]
+**good example is semantic segmentation

@@ 5번째 줄: / 5번째 줄: @@
 ** 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. ''’
-* 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] {{color|red|★}}
+** [https://en.wikipedia.org/wiki/Kronecker_product Kronecker product (wikipedia)]
 ==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).]

@@ 1번째 줄: / 1번째 줄: @@
 ==[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).]==
-* dense prediction?
+* dense prediction : "The goal is to compute a discrete or continuous label for each pixel in the image.”
-** [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).]
+** good example is semantic segmentation

← 이전 판		2017년 3월 23일 (목) 13:41 판
1번째 줄:		1번째 줄:
	==[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).]==
		+	* dense prediction?
		+	** [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).]

← 이전 판		2017년 3월 23일 (목) 13:38 판
1번째 줄:		1번째 줄:
−	[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).]==