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Volumn 9908 LNCS, Issue , 2016, Pages 318-335

Generative image modeling using style and structure adversarial networks

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TEXTURES;

EID: 84990036933     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46493-0_20     Document Type: Conference Paper
Times cited : (454)

References (66)
  • 1
    • 84906483885 scopus 로고    scopus 로고
    • Context as supervisory signal: Discovering objects with predictable context
    • Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
    • Doersch, C., Gupta, A., Efros, A.A.: Context as supervisory signal: discovering objects with predictable context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 362-377. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9-24
    • (2014) ECCV 2014. LNCS , vol.8691 , pp. 362-377
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 2
    • 84973916088 scopus 로고    scopus 로고
    • Unsupervised visual representation learning by context prediction
    • Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)
    • (2015) ICCV
    • Doersch, C.1    Gupta, A.2    Efros, A.A.3
  • 3
    • 84973889989 scopus 로고    scopus 로고
    • Unsupervised learning of visual representations using videos
    • Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: ICCV (2015)
    • (2015) ICCV
    • Wang, X.1    Gupta, A.2
  • 4
    • 84973902378 scopus 로고    scopus 로고
    • Unsupervised learning of spatiotemporally coherent metrics
    • Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y.: Unsupervised learning of spatiotemporally coherent metrics. In: ICCV (2015)
    • (2015) ICCV
    • Goroshin, R.1    Bruna, J.2    Tompson, J.3    Eigen, D.4    Lecun, Y.5
  • 5
    • 84877777295 scopus 로고    scopus 로고
    • Deep learning of invariant features via simulated fixations in video
    • Zou, W.Y., Zhu, S., Ng, A.Y., Yu, K.: Deep learning of invariant features via simulated fixations in video. In: NIPS (2012)
    • (2012) NIPS
    • Zou, W.Y.1    Zhu, S.2    Ng, A.Y.3    Yu, K.4
  • 7
    • 84973880490 scopus 로고    scopus 로고
    • Dense optical flow prediction from a static image
    • Walker, J., Gupta, A., Hebert, M.: Dense optical flow prediction from a static image. In: ICCV (2015)
    • (2015) ICCV
    • Walker, J.1    Gupta, A.2    Hebert, M.3
  • 8
    • 84990049823 scopus 로고    scopus 로고
    • Shuffle and learn: Unsupervised learning using temporal order verification
    • Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: ECCV (2016)
    • (2016) ECCV
    • Misra, I.1    Zitnick, C.L.2    Hebert, M.3
  • 10
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational bayes
    • Kingma, D., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)
    • (2014) ICLR
    • Kingma, D.1    Welling, M.2
  • 12
    • 84970016114 scopus 로고    scopus 로고
    • Generative moment matching networks
    • Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: ICML (2014)
    • (2014) ICML
    • Li, Y.1    Swersky, K.2    Zemel, R.3
  • 14
    • 84867713871 scopus 로고    scopus 로고
    • Indoor segmentation and support inference from RGBD images
    • Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.), Springer, Heidelberg
    • Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746-760. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4-54
    • (2012) ECCV 2012. LNCS , vol.7576 , pp. 746-760
    • Silberman, N.1    Hoiem, D.2    Kohli, P.3    Fergus, R.4
  • 16
    • 84973897623 scopus 로고    scopus 로고
    • Learning image representations tied to ego-motion
    • Jayaraman, D., Grauman, K.: Learning image representations tied to ego-motion. In: ICCV (2015)
    • (2015) ICCV
    • Jayaraman, D.1    Grauman, K.2
  • 18
    • 84977599666 scopus 로고    scopus 로고
    • Supersizing self-supervision: Learning to grasp from 50 k tries and 700 robot hours
    • Pinto, L., Gupta, A.: Supersizing self-supervision: learning to grasp from 50 k tries and 700 robot hours. In: ICRA (2016)
    • (2016) ICRA
    • Pinto, L.1    Gupta, A.2
  • 19
    • 84990042430 scopus 로고    scopus 로고
    • The curious robot: Learning visual representations via physical interactions
    • Pinto, L., Gandhi, D., Han, Y., Park, Y.L., Gupta, A.: The curious robot: learning visual representations via physical interactions. In: ECCV (2016)
    • (2016) ECCV
    • Pinto, L.1    Gandhi, D.2    Han, Y.3    Park, Y.L.4    Gupta, A.5
  • 20
    • 0033285309 scopus 로고    scopus 로고
    • Texture synthesis by non-parametric sampling
    • Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: ICCV (1999)
    • (1999) ICCV
    • Efros, A.A.1    Leung, T.K.2
  • 24
    • 84879850729 scopus 로고    scopus 로고
    • Factored 3-way restricted Boltzmann machines for modeling natural images
    • Ranzato, M.A., Krizhevsky, A., Hinton, G.E.: Factored 3-way restricted Boltzmann machines for modeling natural images. In: AISTATS (2010)
    • (2010) AISTATS
    • Ranzato, M.A.1    Krizhevsky, A.2    Hinton, G.E.3
  • 25
    • 85161976678 scopus 로고    scopus 로고
    • Modeling image patches with a directed hierarchy of Markov random fields
    • Osindero, S., Hinton, G.E.: Modeling image patches with a directed hierarchy of Markov random fields. In: NIPS (2008)
    • (2008) NIPS
    • Osindero, S.1    Hinton, G.E.2
  • 26
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504-507 (2006)
    • (2006) Science , vol.313 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 27
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML (2009)
    • (2009) ICML
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.Y.4
  • 28
    • 85157999846 scopus 로고    scopus 로고
    • Modeling human motion using binary latent variables
    • Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: NIPS (2006)
    • (2006) NIPS
    • Taylor, G.W.1    Hinton, G.E.2    Roweis, S.3
  • 31
    • 84959184995 scopus 로고    scopus 로고
    • Learning to generate chairs with convolutional neural networks
    • Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR (2015)
    • (2015) CVPR
    • Dosovitskiy, A.1    Springenberg, J.T.2    Brox, T.3
  • 35
    • 84965143571 scopus 로고    scopus 로고
    • Deep generative image models using a laplacian pyramid of adversarial networks
    • Denton, E., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. In: NIPS (2015)
    • (2015) NIPS
    • Denton, E.1    Chintala, S.2    Szlam, A.3    Fergus, R.4
  • 39
    • 84959234840 scopus 로고    scopus 로고
    • Designing deep networks for surface normal estimation
    • Wang, X., Fouhey, D.F., Gupta, A.: Designing deep networks for surface normal estimation. In: CVPR (2015)
    • (2015) CVPR
    • Wang, X.1    Fouhey, D.F.2    Gupta, A.3
  • 40
    • 84973897611 scopus 로고    scopus 로고
    • Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
    • Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV (2015)
    • (2015) ICCV
    • Eigen, D.1    Fergus, R.2
  • 41
    • 84898832490 scopus 로고    scopus 로고
    • Data-driven 3D primitives for single image understanding
    • Fouhey, D.F., Gupta, A., Hebert, M.: Data-driven 3D primitives for single image understanding. In: ICCV (2013)
    • (2013) ICCV
    • Fouhey, D.F.1    Gupta, A.2    Hebert, M.3
  • 42
    • 84906517298 scopus 로고    scopus 로고
    • Discriminatively trained dense surface normal estimation
    • Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
    • Ladickỳ, L., Zeisl, B., Pollefeys, M.: Discriminatively trained dense surface normal estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 468-484. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10602-1_31
    • (2014) ECCV 2014. LNCS , vol.8693 , pp. 468-484
    • Ladickỳ, L.1    Zeisl, B.2    Pollefeys, M.3
  • 49
    • 0032025550 scopus 로고    scopus 로고
    • Filters, random fields and maximum entropy (Frame): Towards a unified theory for texture modeling
    • Zhu, S.C., Wu, Y.N., Mumford, D.: Filters, random fields and maximum entropy (frame): towards a unified theory for texture modeling. In: IJCV (1998)
    • (1998) IJCV
    • Zhu, S.C.1    Wu, Y.N.2    Mumford, D.3
  • 51
    • 84893676344 scopus 로고    scopus 로고
    • Rectifier nonlinearities improve neural network acoustic models
    • Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)
    • (2013) ICML
    • Maas, A.L.1    Hannun, A.Y.2    Ng, A.Y.3
  • 53
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 55
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 56
    • 85083951076 scopus 로고    scopus 로고
    • Adam: A method for stochastic optimization
    • Kingma, D., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)
    • (2014) Corr Abs/1412 , pp. 6980
    • Kingma, D.1    Ba, J.2
  • 57
    • 84898793384 scopus 로고    scopus 로고
    • Support surface prediction in indoor scenes
    • Guo, R., Hoiem, D.: Support surface prediction in indoor scenes. In: ICCV (2013)
    • (2013) ICCV
    • Guo, R.1    Hoiem, D.2
  • 58
    • 84937964578 scopus 로고    scopus 로고
    • Learning deep features for scene recognition using places database
    • Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: NIPS (2014)
    • (2014) NIPS
    • Zhou, B.1    Lapedriza, A.2    Xiao, J.3    Torralba, A.4    Oliva, A.5
  • 60
    • 85029359197 scopus 로고    scopus 로고
    • Fast r-cnn
    • Girshick, R.: Fast r-cnn. In: ICCV (2015)
    • (2015) ICCV
    • Girshick, R.1
  • 61
    • 84957966034 scopus 로고    scopus 로고
    • Sun RGB-D: A RGB-D scene understanding benchmark suite
    • Song, S., Lichtenberg, S., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: CVPR (2015)
    • (2015) CVPR
    • Song, S.1    Lichtenberg, S.2    Xiao, J.3
  • 63
    • 84898798081 scopus 로고    scopus 로고
    • SUN3D: A database of big spaces reconstructed using SfM and object labels
    • Xiao, J., Owens, A., Torralba, A.: SUN3D: a database of big spaces reconstructed using SfM and object labels. In: ICCV (2013)
    • (2013) ICCV
    • Xiao, J.1    Owens, A.2    Torralba, A.3
  • 64
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: A holistic representation of the spatial envelope
    • Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42, 145-175 (2011)
    • (2011) IJCV , vol.42 , pp. 145-175
    • Oliva, A.1    Torralba, A.2
  • 65
    • 84906344142 scopus 로고    scopus 로고
    • Learning rich features from RGBD images for object detection and segmentation
    • Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
    • Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGBD images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 345-360. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10584-0_23
    • (2014) ECCV 2014. LNCS , vol.8695 , pp. 345-360
    • Gupta, S.1    Girshick, R.2    Arbeláez, P.3    Malik, J.4
  • 66
    • 84986258326 scopus 로고    scopus 로고
    • Cross modal distillation for supervision transfer
    • Gupta, S., Hoffman, J., Malik, J.: Cross modal distillation for supervision transfer. In: CVPR (2016)
    • (2016) CVPR
    • Gupta, S.1    Hoffman, J.2    Malik, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.