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Deep generative models have shown great promise when it comes to synthesising novel images.
Mathematical models for cellular interactions in development
Lindenmayer, A.: · 1968
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Synthetic topiary
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Comparison of classifier methods: A case study in handwritten digit recognition
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Creation and rendering of realistic trees
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Long Short-Term Memory
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Recurrent nets that time and count
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Reconstructing 3D tree models from instrumented photographs
Shlyakhter, I., Rozenoer, M., Dorsey, J., Teller, S.: · 2001
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Image-based tree modeling
Tan, P., Zeng, G., Wang, J., Kang, S.B., Quan, L.: · 2007
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Knowledge and heuristic-based modeling of laser-scanned trees
Xu, H., Gossett, N., Chen, B.: · 2007
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Approximate image-based tree-modeling using particle flows
Neubert, B., Franken, T., Deussen, O.: · 2007
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Single image tree modeling
Tan, P., Fang, T., Xiao, J., Zhao, P., Quan, L.: · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G.: · 2009
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Automatic reconstruction of tree skeletal structures from point clouds
Livny, Y., Yan, F., Olson, M., Chen, B., Zhang, H., El-Sana, J.: · 2010
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Treesketch: interactive procedural modeling of trees on a tablet
Longay, S., Runions, A., Boudon, F., Prusinkiewicz, P.: · 2012
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Image-based reconstruction and synthesis of dense foliage
Bradley, D., Nowrouzezahrai, D., Beardsley, P.: · 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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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., Bengio, Y.: · 2013
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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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Autoencoding variational bayes
Kingma, D.P., Welling, M.: · 2014
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Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Springenberg, J.T., Brox, T.: · 2015
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Unsupervised representation learning with deep convolutional Generative Adversarial Networks
Radford, A., Metz, L., Chintala, S.: · 2015
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DRAW: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D., Wierstra, D.: · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A.v.d., Bethge, M.: · 2015
Tutorial on variational autoencoders
Doersch, C.: · 2016
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Unsupervised learning of 3D structure from images
Jimenez Rezende, D., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: · 2016
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Learning a predictable and generative vector representation for objects
Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: · 2016
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Pixel recurrent neural networks
Oord, A.v.d., Kalchbrenner, N., Kavukcuoglu, K.: · 2016
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Deep joint demosaicking and denoising
Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: · 2016
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Reflectance modeling by neural texture synthesis
Aittala, M., Aila, T., Lehtinen, J.: · 2016
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Procedural modeling using autoencoder networks
Yumer, M.E., Asente, P., Mech, R., Kara, L.B.: · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., Yan, X.: · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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ADAM: A method for stochastic optimization
Kingma, D.P., Ba, J.L.: · 2015
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Image style transfer using convolutional neural networks
Gatys, L.A., Ecker, A.S., Bethge, M.: · 2016
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Visualizing deep convolutional neural networks using natural pre-images
Mahendran, A., Vedaldi, A.: · 2016
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CVAE-GAN: Fine-grained image generation through asymmetric training
Bao, J., Chen, D., Wen, F., Li, H., Hua, G.: · 2017
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Plug & Play Generative Networks: Conditional iterative generation of images in latent space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., Yosinski, J.: · 2017
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Conditional image synthesis with auxiliary classifier GANs
Odena, A., Olah, C., Shlens, J.: · 2017
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Editable parametric dense foliage from 3d capture
Chaurasia, G., Beardsley, P.: · 2017
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Arjovsky, M., Chintala, S., Bottou, L.: · 2017
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GRASS: Generative recursive autoencoders for shape structures
Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., Guibas, L.: · 2017
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3D-PRNN: Generating shape primitives with Recurrent Neural Networks
Zou, C., Yumer, E., Yang, J., Ceylan, D., Hoiem, D.: · 2017
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The MNIST database of handwritten digits
LeCun, Y., Cortes, C.: · 2017
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