메뉴 건너뛰기




Volumn , Issue , 2016, Pages 865-873

Proximal deep structured models

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; IMAGE DENOISING; RECURRENT NEURAL NETWORKS;

EID: 85019203971     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (83)

References (40)
  • 1
    • 84998953764 scopus 로고    scopus 로고
    • Structured prediction energy networks
    • David Belanger and Andrew McCallum. Structured prediction energy networks. In ICML, 2016.
    • (2016) ICML
    • Belanger, D.1    McCallum, A.2
  • 2
    • 79953201848 scopus 로고    scopus 로고
    • A first-order primal-dual algorithm for convex problems with applications to imaging
    • A. Chambolle and T. Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. JMIV, 2011.
    • (2011) JMIV
    • Chambolle, A.1    Pock, T.2
  • 5
    • 84959197704 scopus 로고    scopus 로고
    • On learning optimized reaction diffusion processes for effective image restoration
    • Y. Chen, W. Yu, and T. Pock. On learning optimized reaction diffusion processes for effective image restoration. In CVPR, 2015.
    • (2015) CVPR
    • Chen, Y.1    Yu, W.2    Pock, T.3
  • 6
    • 34547760736 scopus 로고    scopus 로고
    • Image denoising by sparse 3-d transform-domain collaborative filtering
    • K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. TIP, 2007.
    • (2007) TIP
    • Dabov, K.1    Foi, A.2    Katkovnik, V.3    Egiazarian, K.4
  • 8
    • 84983107941 scopus 로고    scopus 로고
    • Generic methods for optimization-based modeling
    • Justin Domke. Generic methods for optimization-based modeling. In AISTATS, 2012.
    • (2012) AISTATS
    • Domke, J.1
  • 9
    • 84973897611 scopus 로고    scopus 로고
    • Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
    • D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015.
    • (2015) ICCV
    • Eigen, D.1    Fergus, R.2
  • 12
    • 0002211517 scopus 로고
    • A dual algorithm for the solution of nonlinear variational problems via finite element approximation
    • D. Gabay and B. Mercier. A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Computers & Mathematics with Applications, 1976.
    • (1976) Computers & Mathematics with Applications
    • Gabay, D.1    Mercier, B.2
  • 13
    • 0029341230 scopus 로고
    • Nonlinear image recovery with half-quadratic regularization
    • D. Geman and C. Yang. Nonlinear image recovery with half-quadratic regularization. TIP, 1995.
    • (1995) TIP
    • Geman, D.1    Yang, C.2
  • 14
    • 77956515664 scopus 로고    scopus 로고
    • Learning fast approximations of sparse coding
    • Karol Gregor and Yann LeCun. Learning fast approximations of sparse coding. In ICML, 2010.
    • (2010) ICML
    • Gregor, K.1    LeCun, Y.2
  • 16
    • 84973911419 scopus 로고    scopus 로고
    • Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
    • K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In ICCV, 2015.
    • (2015) ICCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 18
  • 20
    • 84858712496 scopus 로고    scopus 로고
    • Fast image deconvolution using hyper-laplacian priors
    • D. Krishnan and R. Fergus. Fast image deconvolution using hyper-laplacian priors. In NIPS, 2009.
    • (2009) NIPS
    • Krishnan, D.1    Fergus, R.2
  • 21
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.3
  • 23
    • 0034850577 scopus 로고    scopus 로고
    • A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
    • D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.
    • (2001) ICCV
    • Martin, D.1    Fowlkes, C.2    Tal, D.3    Malik, J.4
  • 27
    • 80052872903 scopus 로고    scopus 로고
    • Learning message-passing inference Machines for structured prediction
    • S. Ross, D. Munoz, M. Hebert, and J. Bagnell. Learning message-passing inference machines for structured prediction. In CVPR, 2011.
    • (2011) CVPR
    • Ross, S.1    Munoz, D.2    Hebert, M.3    Bagnell, J.4
  • 28
    • 24644467818 scopus 로고    scopus 로고
    • Fields of experts: A framework for learning image priors
    • S. Roth and M. Black. Fields of experts: A framework for learning image priors. In CVPR, 2005.
    • (2005) CVPR
    • Roth, S.1    Black, M.2
  • 30
    • 84911451024 scopus 로고    scopus 로고
    • Shrinkage fields for effective image restoration
    • U. Schmidt and S. Roth. Shrinkage fields for effective image restoration. In CVPR, 2014.
    • (2014) CVPR
    • Schmidt, U.1    Roth, S.2
  • 33
    • 84928547704 scopus 로고    scopus 로고
    • Sequence to sequence learning with neural networks
    • I. Sutskever, O. Vinyals, and Q. Le. Sequence to sequence learning with neural networks. In NIPS, 2014.
    • (2014) NIPS
    • Sutskever, I.1    Vinyals, O.2    Le, Q.3
  • 34
    • 14344250451 scopus 로고    scopus 로고
    • Support vector Machine learning for interdependent and structured output spaces
    • I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004.
    • (2004) ICML
    • Tsochantaridis, I.1    Hofmann, T.2    Joachims, T.3    Altun, Y.4
  • 35
    • 84922968183 scopus 로고    scopus 로고
    • Efficient inference of continuous Markov random fields with polynomial potentials
    • S. Wang, A. Schwing, and R. Urtasun. Efficient inference of continuous markov random fields with polynomial potentials. In NIPS, 2014.
    • (2014) NIPS
    • Wang, S.1    Schwing, A.2    Urtasun, R.3
  • 36
    • 0011952756 scopus 로고    scopus 로고
    • Correctness of belief propagation in Gaussian graphical models of arbitrary topology
    • Y. Weiss and W. Freeman. Correctness of belief propagation in gaussian graphical models of arbitrary topology. Neural computation, 2001.
    • (2001) Neural Computation
    • Weiss, Y.1    Freeman, W.2
  • 37
    • 84887365962 scopus 로고    scopus 로고
    • A convex discrete-continuous approach for Markov random fields
    • C. Zach and P. Kohli. A convex discrete-continuous approach for markov random fields. In ECCV. 2012.
    • (2012) ECCV
    • Zach, C.1    Kohli, P.2
  • 38
    • 84952674245 scopus 로고    scopus 로고
    • Computing the stereo matching cost with a convolutional neural network
    • J. Zbontar and Y. LeCun. Computing the stereo matching cost with a convolutional neural network. In CVPR, 2015.
    • (2015) CVPR
    • Zbontar, J.1    LeCun, Y.2
  • 40
    • 84856650948 scopus 로고    scopus 로고
    • From learning models of natural image patches to whole image restoration
    • D. Zoran and Y. Weiss. From learning models of natural image patches to whole image restoration. In ICCV, 2011.
    • (2011) ICCV
    • Zoran, D.1    Weiss, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.