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Volumn 2016-December, Issue , 2016, Pages 2536-2544

Context Encoders: Feature Learning by Inpainting

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LEARNING ALGORITHMS; NEURAL NETWORKS; PIXELS; SEMANTICS;

EID: 84986294165     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.278     Document Type: Conference Paper
Times cited : (6048)

References (40)
  • 5
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • R. Collobert and J.Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML, 2008.
    • (2008) ICML
    • Collobert, R.1    Weston, J.2
  • 6
    • 84959227156 scopus 로고    scopus 로고
    • Context as supervisory signal: Discovering objects with predictable context
    • C. Doersch, A. Gupta, and A. A. Efros. Context as supervisory signal: Discovering objects with predictable context. In ECCV, 2014.
    • (2014) ECCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 7
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • C. Doersch, A. Gupta, and A. A. Efros. Unsupervised visual representation learning by context prediction. ICCV, 2015.
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 10
    • 84959184995 scopus 로고    scopus 로고
    • Learning to generate chairs with convolutional neural networks
    • A. Dosovitskiy, J. T. Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. CVPR, 2015.
    • (2015) CVPR
    • Dosovitskiy, A.1    Springenberg, J.T.2    Brox, T.3
  • 11
    • 0033285309 scopus 로고    scopus 로고
    • Texture synthesis by nonparametric sampling
    • A. Efros and T. K. Leung. Texture synthesis by nonparametric sampling. In ICCV, 1999.
    • (1999) ICCV
    • Efros, A.1    Leung, T.K.2
  • 13
    • 0019152630 scopus 로고
    • Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
    • K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 1980.
    • (1980) Biological Cybernetics
    • Fukushima, K.1
  • 14
  • 15
    • 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
  • 19
    • 36949030203 scopus 로고    scopus 로고
    • Scene completion using millions of photographs
    • J. Hays and A. A. Efros. Scene completion using millions of photographs. SIGGRAPH, 2007.
    • (2007) SIGGRAPH
    • Hays, J.1    Efros, A.A.2
  • 20
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006.
    • (2006) Science
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 21
    • 84973897623 scopus 로고    scopus 로고
    • Learning image representations tied to ego-motion
    • D. Jayaraman and K. Grauman. Learning image representations tied to ego-motion. In ICCV, 2015.
    • (2015) ICCV
    • Jayaraman, D.1    Grauman, K.2
  • 23
    • 85083951076 scopus 로고    scopus 로고
    • Adam: A method for stochastic optimization
    • D. Kingma and J. Ba. Adam: A method for stochastic optimization. ICLR, 2015.
    • (2015) ICLR
    • Kingma, D.1    Ba, J.2
  • 24
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational bayes
    • D. P. Kingma and M. Welling. Auto-encoding variational bayes. ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 25
    • 85083952350 scopus 로고    scopus 로고
    • Datadependent initializations of convolutional neural networks
    • P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell. Datadependent initializations of convolutional neural networks. ICLR, 2016.
    • (2016) ICLR
    • Krähenbühl, P.1    Doersch, C.2    Donahue, J.3    Darrell, T.4
  • 26
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 28
    • 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
  • 29
    • 84858736606 scopus 로고    scopus 로고
    • Beyond categories: The visual memex model for reasoning about object relationships
    • T. Malisiewicz and A. Efros. Beyond categories: The visual memex model for reasoning about object relationships. In NIPS, 2009.
    • (2009) NIPS
    • Malisiewicz, T.1    Efros, A.2
  • 30
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
    • (2013) NIPS
    • Mikolov, T.1    Sutskever, I.2    Chen, K.3    Corrado, G.S.4    Dean, J.5
  • 31
    • 33749236045 scopus 로고    scopus 로고
    • Building the GIST of a scene: The role of global image features in recognition
    • A. Oliva and A. Torralba. Building the gist of a scene: The role of global image features in recognition. Progress in brain research, 2006.
    • (2006) Progress in Brain Research
    • Oliva, A.1    Torralba, A.2
  • 33
    • 85083950271 scopus 로고    scopus 로고
    • Unsupervised representation learning with deep convolutional generative adversarial networks
    • A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ICLR, 2016.
    • (2016) ICLR
    • Radford, A.1    Metz, L.2    Chintala, S.3
  • 34
    • 84973912614 scopus 로고    scopus 로고
    • Learning temporal embeddings for complex video analysis
    • V. Ramanathan, K. Tang, G. Mori, and L. Fei-Fei. Learning temporal embeddings for complex video analysis. ICCV, 2015.
    • (2015) ICCV
    • Ramanathan, V.1    Tang, K.2    Mori, G.3    Fei-Fei, L.4
  • 36
    • 84876218917 scopus 로고    scopus 로고
    • Disentangling factors of variation for facial expression recognition
    • S. Rifai, Y. Bengio, A. Courville, P. Vincent, and M. Mirza. Disentangling factors of variation for facial expression recognition. In ECCV, 2012.
    • (2012) ECCV
    • Rifai, S.1    Bengio, Y.2    Courville, A.3    Vincent, P.4    Mirza, M.5
  • 38
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.
    • (2008) ICML
    • Vincent, P.1    Larochelle, H.2    Bengio, Y.3    Manzagol, P.-A.4
  • 39
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • X.Wang and A. Gupta. Unsupervised learning of visual representations using videos. ICCV, 2015.
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 40
    • 85009899017 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
    • (2014) ECCV
    • Zeiler, M.D.1    Fergus, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.