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Volumn 48, Issue 8, 2015, Pages 2645-2655

Detection guided deconvolutional network for hierarchical feature learning

Author keywords

Deep leaning; Image representation; Object recognition

Indexed keywords

DEEP LEARNING; FEATURE EXTRACTION; IMAGE CODING; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; NETWORK LAYERS; OBJECT DETECTION; OBJECT RECOGNITION; SUPPORT VECTOR MACHINES;

EID: 84928289917     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.02.002     Document Type: Article
Times cited : (15)

References (38)
  • 1
    • 56749159833 scopus 로고    scopus 로고
    • Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks
    • A. Ahmed, K. Yu, W. Xu, Y. Gong, E. Xing, Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks, in: European Conference on Computer Vision (ECCV), 2008, pp. 69-82.
    • (2008) European Conference on Computer Vision (ECCV) , pp. 69-82
    • Ahmed, A.1    Yu, K.2    Xu, W.3    Gong, Y.4    Xing, E.5
  • 2
    • 85014561619 scopus 로고    scopus 로고
    • A fast iterative shrinkage-thresholding algorithm for linear inverse problems
    • A. Beck, and M. Teboulle A fast iterative shrinkage-thresholding algorithm for linear inverse problems SIAM J. Imaging Sci. 2 2009 183 202
    • (2009) SIAM J. Imaging Sci. , vol.2 , pp. 183-202
    • Beck, A.1    Teboulle, M.2
  • 6
    • 0021182724 scopus 로고
    • Stimulus-selective properties of inferior temporal neurons in the macaque
    • R. Desimone, T.D. Albright, C.G. Gross, and C. Bruce Stimulus-selective properties of inferior temporal neurons in the macaque J. Neurosci. 4 1984 2051 2062
    • (1984) J. Neurosci. , vol.4 , pp. 2051-2062
    • Desimone, R.1    Albright, T.D.2    Gross, C.G.3    Bruce, C.4
  • 8
    • 34047174674 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples an incremental Bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, and P. Perona Learning generative visual models from few training examples an incremental Bayesian approach tested on 101 object categories Comput. Vis. Image Underst. 106 2007 59 70
    • (2007) Comput. Vis. Image Underst. , vol.106 , pp. 59-70
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 12
    • 34948904828 scopus 로고    scopus 로고
    • Caltech-256 object category dataset
    • California Institute of Technology
    • G. Griffin, A. Holub, P. Perona, Caltech-256 object category dataset, Technical Report 7694, California Institute of Technology, 2007.
    • (2007) Technical Report 7694
    • Griffin, G.1    Holub, A.2    Perona, P.3
  • 14
  • 15
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • G.E. Hinton, and S. Osindero A fast learning algorithm for deep belief nets Neural Comput. 18 2006 1527 1554
    • (2006) Neural Comput. , vol.18 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2
  • 17
    • 0037780988 scopus 로고    scopus 로고
    • Modeling receptive fields with non-negative sparse coding
    • P.O. Hoyer Modeling receptive fields with non-negative sparse coding Neurocomputing 52-54 2003 547 552
    • (2003) Neurocomputing , vol.52-54 , pp. 547-552
    • Hoyer, P.O.1
  • 19
    • 33645410496 scopus 로고
    • Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex
    • D.H. Hubel, and T.N. Wiesel Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex J. Physiol. 160 1962 106 154
    • (1962) J. Physiol. , vol.160 , pp. 106-154
    • Hubel, D.H.1    Wiesel, T.N.2
  • 23
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proc. IEEE 86 1998 2278 2324
    • (1998) Proc. IEEE , vol.86 , pp. 2278-2324
    • Lecun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 24
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • H. Lee, R. Grosse, R. Ranganath, A.Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: International Conference on Machine Learning (ICML), 2009, pp. 609-616.
    • (2009) International Conference on Machine Learning (ICML) , pp. 609-616
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.Y.4
  • 25
    • 85162513516 scopus 로고    scopus 로고
    • Object bank: a high-level image representation for scene classification and semantic feature sparsification
    • L.J. Li, H. Su, E.P. Xing, L. Fei-Fei, Object bank: a high-level image representation for scene classification and semantic feature sparsification, in: Advances in Neural Information Processing Systems (NIPS), 2010, pp. 1378-1386.
    • (2010) Advances in Neural Information Processing Systems (NIPS) , pp. 1378-1386
    • Li, L.J.1    Su, H.2    Xing, E.P.3    Fei-Fei, L.4
  • 26
    • 21344435992 scopus 로고    scopus 로고
    • Invariant visual representation by single neurons in the human brain
    • R.Q. Quiroga, L. Reddy, G. Kreiman, C. Koch, and I. Fried Invariant visual representation by single neurons in the human brain Nature 435 2005 1102 1107
    • (2005) Nature , vol.435 , pp. 1102-1107
    • Quiroga, R.Q.1    Reddy, L.2    Kreiman, G.3    Koch, C.4    Fried, I.5
  • 27
    • 0033316361 scopus 로고    scopus 로고
    • Hierarchical models of object recognition in cortex
    • M. Riesenhuber, and T. Poggio Hierarchical models of object recognition in cortex Nat. Neurosci. 2 1999 1019 1025
    • (1999) Nat. Neurosci. , vol.2 , pp. 1019-1025
    • Riesenhuber, M.1    Poggio, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.