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Volumn 07-12-June-2015, Issue , 2015, Pages 427-436

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; NEURAL NETWORKS; WHITE NOISE;

EID: 84946206172     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298640     Document Type: Conference Paper
Times cited : (3289)

References (32)
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    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
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    • Principles of modularity, regularity, and hierarchy for scalable systems
    • H. Lipson. Principles of modularity, regularity, and hierarchy for scalable systems. Journal of Biological Physics and Chemistry, 7(4):125, 2007
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    • Lipson, H.1
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    • 0036594106 scopus 로고    scopus 로고
    • Evolving neural networks through augmenting topologies
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    • Stanley, K.1    Miikkulainen, R.2
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    • Compositional pattern producing networks: A novel abstraction of development
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    • Stanley, K.O.1
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    • A taxonomy for artificial embryogeny
    • K. O. Stanley and R. Miikku1ainen. A taxonomy for artificial embryogeny. Artificial Life, 9(2):93-130, 2003
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    • Stanley, K.O.1    Miikkulainen, R.2
  • 32
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    • How transferable are features in deep neural networks
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    • J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3320-3328. CurranA ssociates, Inc., Dec. 2014.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.