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Volumn , Issue , 2016, Pages

Data-dependent initializations of convolutional neural networks

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; NEURAL NETWORKS; OBJECT DETECTION; VISION;

EID: 85083952350     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (83)

References (14)
  • 1
    • 84973926501 scopus 로고    scopus 로고
    • Learning to see by moving
    • 7, 8
    • Agrawal, Pulkit, Carreira, Joao, and Malik, Jitendra. Learning to see by moving. ICCV, 2015. 7, 8
    • (2015) ICCV
    • Agrawal, P.1    Carreira, J.2    Malik, J.3
  • 3
    • 84872575253 scopus 로고    scopus 로고
    • Learning feature representations with k-means
    • Springer, 5
    • Coates, Adam and Ng, Andrew Y. Learning feature representations with k-means. In Neural Networks: Tricks of the Trade, pp. 561–580. Springer, 2012. 5
    • (2012) Neural Networks: Tricks of the Trade , pp. 561-580
    • Coates, A.1    Ng, A.Y.2
  • 4
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • 6, 8, 11
    • Doersch, Carl, Gupta, Abhinav, and Efros, Alexei A. Unsupervised visual representation learning by context prediction. ICCV, 2015. 6, 8, 11
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 5
    • 84921069139 scopus 로고    scopus 로고
    • The Pascal Visual Object Classes challenge: A retrospective
    • 5, 6
    • Everingham, Mark, Eslami, SM Ali, Van Gool, Luc, Williams, Christopher KI, Winn, John, and Zisserman, Andrew. The Pascal Visual Object Classes challenge: A retrospective. IJCV, 111(1): 98–136, 2014. 5, 6
    • (2014) IJCV , vol.111 , Issue.1 , pp. 98-136
    • Everingham, M.1    Eslami, S.M.A.2    Van Gool, L.3    Williams, C.K.I.4    Winn, J.5    Zisserman, A.6
  • 7
    • 84862277874 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • 2, 7, 8, 9
    • Glorot, Xavier and Bengio, Yoshua. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, pp. 249–256, 2010. 2, 7, 8, 9
    • (2010) AISTATS , pp. 249-256
    • Glorot, X.1    Bengio, Y.2
  • 8
    • 84973911419 scopus 로고    scopus 로고
    • Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
    • 2, 7, 8, 12
    • He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In ICCV, 2015. 2, 7, 8, 12
    • (2015) ICCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 9
    • 84969584486 scopus 로고    scopus 로고
    • Batch normalization: Accelerating deep network training by reducing internal covariate shift
    • 2, 7
    • Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015. 2, 7
    • (2015) ICML
    • Ioffe, S.1    Szegedy, C.2
  • 11
    • 85083951076 scopus 로고    scopus 로고
    • ADaM: A method for stochastic optimization
    • 7
    • Kingma, Diederik and Ba, Jimmy. Adam: A method for stochastic optimization. ICLR, 2015. 7
    • (2015) ICLR
    • Kingma, D.1    Ba, J.2
  • 12
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • 2, 6, 8
    • Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. 2, 6, 8
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.