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2016 | OriginalPaper | Buchkapitel

Generative Image Modeling Using Style and Structure Adversarial Networks

verfasst von : Xiaolong Wang, Abhinav Gupta

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution. However, these approaches ignore the most basic principle of image formation: images are product of: (a) Structure: the underlying 3D model; (b) Style: the texture mapped onto structure. In this paper, we factorize the image generation process and propose Style and Structure Generative Adversarial Network (\({\text {S}^2}\)-GAN). Our \({\text {S}^2}\)-GAN has two components: the Structure-GAN generates a surface normal map; the Style-GAN takes the surface normal map as input and generates the 2D image. Apart from a real vs. generated loss function, we use an additional loss with computed surface normals from generated images. The two GANs are first trained independently, and then merged together via joint learning. We show our \({\text {S}^2}\)-GAN model is interpretable, generates more realistic images and can be used to learn unsupervised RGBD representations.

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Vorheriges Kapitel Top-Down Learning for Structured Labeling with Convolutional Pseudoprior

Nächstes Kapitel Joint Learning of Semantic and Latent Attributes

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

Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)

Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: ICCV (2015)

Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y.: Unsupervised learning of spatiotemporally coherent metrics. In: ICCV (2015)

Zou, W.Y., Zhu, S., Ng, A.Y., Yu, K.: Deep learning of invariant features via simulated fixations in video. In: NIPS (2012)

Li, Y., Paluri, M., Rehg, J.M., Dollar, P.: Unsupervised learning of edges. In: CVPR (2016)

Walker, J., Gupta, A., Hebert, M.: Dense optical flow prediction from a static image. In: ICCV (2015)

Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: ECCV (2016)

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS (2014)

10.

Kingma, D., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)

11.

Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: Draw: a recurrent neural network for image generation. CoRR abs/1502.04623 (2015)

12.

Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: ICML (2014)

13.

Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs/1511.06434 (2015)

14.

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

15.

Agrawal, P., Carreira, J., Malik, J.: Learning to see by moving. In: ICCV (2015)

16.

Jayaraman, D., Grauman, K.: Learning image representations tied to ego-motion. In: ICCV (2015)

17.

Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E., Freeman, W.: Visually indicated sounds. In: CVPR (2016)

18.

Pinto, L., Gupta, A.: Supersizing self-supervision: learning to grasp from 50 k tries and 700 robot hours. In: ICRA (2016)

19.

Pinto, L., Gandhi, D., Han, Y., Park, Y.L., Gupta, A.: The curious robot: learning visual representations via physical interactions. In: ECCV (2016)

20.

Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: ICCV (1999)

21.

Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. In: Computer Graphics and Applications (2002)

22.

Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS (2007)

23.

Le, Q.V., Ranzato, M.A., Monga, R., Devin, M., Chen, K., Corrado, G.S., Dean, J., Ng, A.Y.: Building high-level features using large scale unsupervised learning. In: ICML (2012)

24.

Ranzato, M.A., Krizhevsky, A., Hinton, G.E.: Factored 3-way restricted Boltzmann machines for modeling natural images. In: AISTATS (2010)

25.

Osindero, S., Hinton, G.E.: Modeling image patches with a directed hierarchy of Markov random fields. In: NIPS (2008)

26.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRefMATH

27.

Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML (2009)

28.

Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: NIPS (2006)

29.

Mansimov, E., Parisotto, E., Ba, J.L., Salakhutdinov, R.: Generating images from captions with attention. CoRR abs/1511.02793 (2015)

30.

Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.B.: Deep convolutional inverse graphics network. In: NIPS (2015)

31.

Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR (2015)

32.

Tatarchenko, M., Dosovitskiy, A., Brox, T.: Single-view to multi-view: reconstructing unseen views with a convolutional network. CoRR abs/1511.06702 (2015)

33.

Theis, L., Bethge, M.: Generative image modeling using spatial LSTMs. CoRR abs/1506.03478 (2015)

34.

Oord, A.V.D., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. CoRR abs/1601.06759 (2016)

35.

Denton, E., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. In: NIPS (2015)

36.

Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR abs/1411.1784 (2014)

37.

Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. CoRR abs/1511.05440 (2015)

38.

Im, D.J., Kim, C.D., Jiang, H., Memisevic, R.: Generating images with recurrent adversarial networks. CoRR abs/1602.05110 (2016)

39.

Wang, X., Fouhey, D.F., Gupta, A.: Designing deep networks for surface normal estimation. In: CVPR (2015)

40.

Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV (2015)

41.

Fouhey, D.F., Gupta, A., Hebert, M.: Data-driven 3D primitives for single image understanding. In: ICCV (2013)

42.

Ladický, 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

43.

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.J.: Adversarial autoencoders. CoRR abs/1511.05644 (2015)

44.

Larsen, A.B.L., Sønderby, S.K., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. CoRR abs/1512.09300 (2015)

45.

Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. CoRR abs/1602.02644 (2016)

46.

Barrow, H.G., Tenenbaum, J.M.: Recovering intrinsic scene characteristics from images. In: Computer Vision Systems (1978)

47.

Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. In: Neural Computation (2000)

48.

Fouhey, D.F., Hussain, W., Gupta, A., Hebert, M.: Single image 3D without a single 3D image. In: ICCV (2015)

49.

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)

50.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015)

51.

Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)

52.

Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. CoRR abs/1505.00853 (2015)

53.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

54.

Ladický, L., Shi, J., Pollefeys, M.: Pulling things out of perspective. In: CVPR (2014)

55.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)

56.

Kingma, D., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)

57.

Guo, R., Hoiem, D.: Support surface prediction in indoor scenes. In: ICCV (2013)

58.

Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: NIPS (2014)

59.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRef

60.

Girshick, R.: Fast r-cnn. In: ICCV (2015)

61.

Song, S., Lichtenberg, S., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: CVPR (2015)

62.

Janoch, A., Karayev, S., Jia, Y., Barron, J., Fritz, M., Saenko, K., Darrell, T.: A category-level 3-D object dataset: Putting the kinect to work. In: Workshop on Consumer Depth Cameras in Computer Vision (with ICCV) (2011)

63.

Xiao, J., Owens, A., Torralba, A.: SUN3D: a database of big spaces reconstructed using SfM and object labels. In: ICCV (2013)

64.

Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42, 145–175 (2011)CrossRefMATH

65.

Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D 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

66.

Gupta, S., Hoffman, J., Malik, J.: Cross modal distillation for supervision transfer. In: CVPR (2016)

Titel: Generative Image Modeling Using Style and Structure Adversarial Networks
verfasst von: Xiaolong Wang
Abhinav Gupta
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46492-3

Electronic ISBN: 978-3-319-46493-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46493-0_20

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