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Volumn 2016-December, Issue , 2016, Pages 770-778

Deep residual learning for image recognition

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; COMPUTER VISION; IMAGE RECOGNITION; OBJECT DETECTION;

EID: 84986274465     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.90     Document Type: Conference Paper
Times cited : (199138)

References (49)
  • 1
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5 (2): 157-166, 1994.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.2 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 4
    • 84898420173 scopus 로고    scopus 로고
    • The devil is in the details: An evaluation of recent feature encoding methods
    • K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: An evaluation of recent feature encoding methods. In BMVC, 2011.
    • (2011) BMVC
    • Chatfield, K.1    Lempitsky, V.2    Vedaldi, A.3    Zisserman, A.4
  • 6
    • 85029359197 scopus 로고    scopus 로고
    • Fast R-CNN
    • R. Girshick. Fast R-CNN. In ICCV, 2015.
    • (2015) ICCV
    • Girshick, R.1
  • 7
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 8
    • 79951563340 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, 2010.
    • (2010) AISTATS
    • Glorot, X.1    Bengio, Y.2
  • 10
    • 84959197642 scopus 로고    scopus 로고
    • Convolutional neural networks at constrained time cost
    • K. He and J. Sun. Convolutional neural networks at constrained time cost. In CVPR, 2015.
    • (2015) CVPR
    • He, K.1    Sun, J.2
  • 11
    • 85009918748 scopus 로고    scopus 로고
    • Spatial pyramid pooling in deep convolutional networks for visual recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014.
    • (2014) ECCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 12
    • 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
  • 16
    • 84969584486 scopus 로고    scopus 로고
    • Batch normalization: Accelerating deep network training by reducing internal covariate shift
    • S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
    • (2015) ICML
    • Ioffe, S.1    Szegedy, C.2
  • 17
    • 78649317568 scopus 로고    scopus 로고
    • Product quantization for nearest neighbor search
    • H. Jegou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. TPAMI, 33, 2011.
    • (2011) TPAMI , vol.33
    • Jegou, H.1    Douze, M.2    Schmid, C.3
  • 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
  • 27
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 28
    • 84930634427 scopus 로고    scopus 로고
    • On the number of linear regions of deep neural networks
    • G. Montúfar, R. Pascanu, K. Cho, and Y. Bengio. On the number of linear regions of deep neural networks. In NIPS, 2014.
    • (2014) NIPS
    • Montúfar, G.1    Pascanu, R.2    Cho, K.3    Bengio, Y.4
  • 29
    • 77956509090 scopus 로고    scopus 로고
    • Rectified linear units improve restricted boltzmann machines
    • V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010.
    • (2010) ICML
    • Nair, V.1    Hinton, G.E.2
  • 30
    • 34948815101 scopus 로고    scopus 로고
    • Fisher kernels on visual vocabularies for image categorization
    • F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007.
    • (2007) CVPR
    • Perronnin, F.1    Dance, C.2
  • 31
    • 84893414160 scopus 로고    scopus 로고
    • Deep learning made easier by linear transformations in perceptrons
    • T. Raiko, H. Valpola, and Y. LeCun. Deep learning made easier by linear transformations in perceptrons. In AISTATS, 2012.
    • (2012) AISTATS
    • Raiko, T.1    Valpola, H.2    LeCun, Y.3
  • 32
    • 84960980241 scopus 로고    scopus 로고
    • Faster R-CNN: Towards real-time object detection with region proposal networks
    • S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS, 2015.
    • (2015) NIPS
    • Ren, S.1    He, K.2    Girshick, R.3    Sun, J.4
  • 38
    • 0038231917 scopus 로고    scopus 로고
    • Centering neural network gradient factors
    • Springer
    • N. N. Schraudolph. Centering neural network gradient factors. In Neural Networks: Tricks of the Trade, pages 207-226. Springer, 1998.
    • (1998) Neural Networks: Tricks of the Trade , pp. 207-226
    • Schraudolph, N.N.1
  • 39
    • 85083951635 scopus 로고    scopus 로고
    • Overfeat: Integrated recognition, localization and detection using convolutional networks
    • P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le-Cun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
    • (2014) ICLR
    • Sermanet, P.1    Eigen, D.2    Zhang, X.3    Mathieu, M.4    Fergus, R.5    Le-Cun, Y.6
  • 40
    • 85083953063 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
    • (2015) ICLR
    • Simonyan, K.1    Zisserman, A.2
  • 44
    • 0025448712 scopus 로고
    • Fast surface interpolation using hierarchical basis functions
    • R. Szeliski. Fast surface interpolation using hierarchical basis functions. TPAMI, 1990.
    • (1990) TPAMI
    • Szeliski, R.1
  • 45
    • 77953983762 scopus 로고    scopus 로고
    • Locally adapted hierarchical basis preconditioning
    • R. Szeliski. Locally adapted hierarchical basis preconditioning. In SIGGRAPH, 2006.
    • (2006) SIGGRAPH
    • Szeliski, R.1
  • 46
    • 85083953220 scopus 로고    scopus 로고
    • Pushing stochastic gradient towards second-order methods-backpropagation learning with transformations in nonlinearities
    • T. Vatanen, T. Raiko, H. Valpola, and Y. LeCun. Pushing stochastic gradient towards second-order methods-backpropagation learning with transformations in nonlinearities. In Neural Information Processing, 2013.
    • (2013) Neural Information Processing
    • Vatanen, T.1    Raiko, T.2    Valpola, H.3    LeCun, Y.4
  • 49
    • 84937902251 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional neural networks
    • M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. In ECCV, 2014.
    • (2014) ECCV
    • Zeiler, M.D.1    Fergus, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.