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To truly understand the visual world our models should be able not only to recognize images but also generate them.
Learning task-dependent distributed representations by backpropagation through structure
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Supervised neural networks for the classification of structures
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A general framework for adaptive processing of data structures
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Parsing natural scenes and natural language with recursive neural networks
R. Socher, C. C. Lin, C. Manning, and A. Y. Ng · 2011
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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Learning spatial knowledge for text to 3d scene generation
A. X. Chang, M. Savva, and C. D. Manning · 2014
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Conditional generative adversarial nets for convolutional face generation
J. Gauthier · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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Text to 3d scene generation with rich lexical grounding
A. Chang, W. Monroe, M. Savva, C. Potts, and C. D. Manning · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Image retrieval using scene graphs
J. Johnson, R. Krishna, M. Stark, L.-J. Li, D. Shamma, M. Bernstein, and L. Fei-Fei · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Picture: A probabilistic programming language for scene perception
T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2015
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Generative adversarial text-to-image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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A note on the evaluation of generative models
L. Theis, A. v. d. Oord, and M. Bethge · 2016
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Conditional image generation with PixelCNN decoders
A. van den Oord, N. Kalchbrenner, L. Espeholt, k. kavukcuoglu, O. Vinyals, and A. Graves · 2016
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A theory of generative convnet
J. Xie, Y. Lu, S.-C. Zhu, and Y. Wu · 2016
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Photographic image synthesis with cascaded refinement networks
Q. Chen and V. Koltun · 2017
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Generating semantically precise scene graphs from textual descriptions for improved image retrieval
S. Schuster, R. Krishna, A. Chang, L. Fei-Fei, and C. D. Manning · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Line: Large-scale information network embedding
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
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Spice: Semantic propositional image caption evaluation
P. Anderson, B. Fernando, M. Johnson, and S. Gould · 2016
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Coco-stuff: Thing and stuff classes in context
H. Caesar, J. Uijlings, and V. Ferrari · 2016
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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C. Jiang, Y. Zhu, S. Qi, S. Huang, J. Lin, X. Guo, L.-F. Yu, D. Terzopoulos, and S.-C. Zhu · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, et al · 2017
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Improved image captioning via policy gradient optimization of spider
S. Liu, Z. Zhu, N. Ye, S. Guadarrama, and K. Murphy · 2017
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Pixels to graphs by associative embedding
A. Newell and J. Deng · 2017
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Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2017
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Parallel multiscale autoregressive density estimation
S. E. Reed, A. van den Oord, N. Kalchbrenner, S. Gómez, Z. Wang, D. Belov, and N. de Freitas · 2017
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Premise selection for theorem proving by deep graph embedding
M. Wang, Y. Tang, J. Wang, and J. Deng · 2017
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Neural scene de-rendering
J. Wu, J. B. Tenenbaum, and P. Kohli · 2017
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Scene graph generation by iterative message passing
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei · 2017
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On support relations and semantic scene graphs
M. Y. Yang, W. Liao, H. Ackermann, and B. Rosenhahn · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas · 2017
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