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Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution.
Recovering intrinsic scene characteristics from images
Barrow, H.G., Tenenbaum, J.M.: · 1978
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Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling
Zhu, S.C., Wu, Y.N., Mumford, D.: · 1998
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Texture synthesis by non-parametric sampling
Efros, A.A., Leung, T.K.: · 1999
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Separating style and content with bilinear models
Tenenbaum, J.B., Freeman, W.T.: · 2000
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Example-based super-resolution
Freeman, W.T., Jones, T.R., Pasztor, E.C.: · 2002
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Reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R.: · 2006
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Modeling human motion using binary latent variables
Taylor, G.W., Hinton, G.E., Roweis, S.: · 2006
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: · 2007
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Modeling image patches with a directed hierarchy of markov random fields
Osindero, S., Hinton, G.E.: · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: · 2009
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Factored 3-way restricted boltzmann machines for modeling natural images
Ranzato, M.A., Krizhevsky, A., Hinton, G.E.: · 2010
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A category-level 3-d object dataset: Putting the kinect to work
Janoch, A., Karayev, S., Jia, Y., Barron, J., Fritz, M., Saenko, K., Darrell, T.: · 2011
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Modeling the shape of the scene: A holistic representation of the spatial envelope
Oliva, A., Torralba, A.: · 2011
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Deep learning of invariant features via simulated fixations in video
Zou, W.Y., Zhu, S., Ng, A.Y., Yu, K.: · 2012
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Indoor segmentation and support inference from RGBD images
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: · 2012
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Building high-level features using large scale unsupervised learning
Le, Q.V., Ranzato, M.A., Monga, R., Devin, M., Chen, K., Corrado, G.S., Dean, J., Ng, A.Y.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Data-driven 3D primitives for single image understanding
Fouhey, D.F., Gupta, A., Hebert, M.: · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A.L., Hannun, A.Y., Ng, A.Y.: · 2013
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Support surface prediction in indoor scenes
Guo, R., Hoiem, D.: · 2013
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Sun3d: A database of big spaces reconstructed using sfm and object labels
Xiao, J., Owens, A., Torralba, A.: · 2013
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Context as supervisory signal: Discovering objects with predictable context
Doersch, C., Gupta, A., Efros, A.A.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Auto-encoding variational bayes
Kingma, D., Welling, M.: · 2014
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Generative moment matching networks
Li, Y., Swersky, K., Zemel, R.: · 2014
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Conditional generative adversarial nets
Mirza, M., Osindero, S.: · 2014
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Discriminatively trained dense surface normal estimation
Ladický, L., Zeisl, B., Pollefeys, M.: · 2014
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Pulling things out of perspective
Ladický, L., Shi, J., Pollefeys, M.: · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E., Chintala, S., Szlam, A., Fergus, R.: · 2015
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2015
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Designing deep networks for surface normal estimation
Wang, X., Fouhey, D.F., Gupta, A.: · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D., Fergus, R.: · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.J.: · 2015
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Kingma, D., Ba, J.: · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
Cited alongside, same era.
Learning rich features from rgb-d images for object detection and segmentation
Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: · 2014
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., Efros, A.A.: · 2015
Cited alongside, same era.
Unsupervised learning of visual representations using videos
Wang, X., Gupta, A.: · 2015
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Unsupervised learning of spatiotemporally coherent metrics
Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y.: · 2015
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Larsen, A.B.L., Sønderby, S.K., Winther, O.: · 2015
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Single image 3d without a single 3d image
Fouhey, D.F., Hussain, W., Gupta, A., Hebert, M.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Empirical evaluation of rectified activations in convolutional network
Xu, B., Wang, N., Chen, T., Li, M.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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ImageNet Large Scale Visual Recognition Challenge
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.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Song, S., Lichtenberg, S., Xiao, J.: · 2015
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Unsupervised learning of edges
Li, Y., Paluri, M., Rehg, J.M., Dollar, P.: · 2016
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Shuffle and learn: Unsupervised learning using temporal order verification
Misra, I., Zitnick, C.L., Hebert, M.: · 2016
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Visually indicated sounds
Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E., Freeman, W.: · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Pinto, L., Gupta, A.: · 2016
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The curious robot: Learning visual representations via physical interactions
Pinto, L., Gandhi, D., Han, Y., Park, Y.L., Gupta, A.: · 2016
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Pixel recurrent neural networks
Oord, A.V.D., Kalchbrenner, N., Kavukcuoglu, K.: · 2016
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Generating images with recurrent adversarial networks
Im, D.J., Kim, C.D., Jiang, H., Memisevic, R.: · 2016
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A., Brox, T.: · 2016
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Cross modal distillation for supervision transfer
Gupta, S., Hoffman, J., Malik, J.: · 2016
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