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Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result.
Digital Image Warping
Wolberg, G.: · 1990
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A limited memory algorithm for bound constrained optimization
Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: · 1995
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B.A., Field, D.J.: · 1996
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View morphing
Seitz, S.M., Dyer, C.R.: · 1996
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As-rigid-as-possible shape interpolation
Alexa, M., Cohen-Or, D., Levin, D.: · 2000
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A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., Simoncelli, E.P.: · 2000
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Color transfer between images
Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: · 2001
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Colorization using optimization
Levin, A., Lischinski, D., Weiss, Y.: · 2004
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High accuracy optical flow estimation based on a theory for warping
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: · 2004
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Fields of Experts: A Framework for Learning Image Priors
Roth, S., Black, M.J.: · 2005
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Bruhn, A., Weickert, J., Schnörr, C.: · 2005
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
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Reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R.: · 2006
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: · 2008
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A system for retargeting of streaming video
Krähenbühl, P., Lang, M., Hornung, A., Gross, M.: · 2009
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Patchmatch: A randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.: · 2009
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Deep boltzmann machines
Salakhutdinov, R., Hinton, G.E.: · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Regenerative morphing
Shechtman, E., Rav-Acha, A., Irani, M., Seitz, S.: · 2010
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Guided image filtering
He, K., Sun, J., Tang, X.: · 2010
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Synthesizing structured image hybrids
Risser, E., Han, C., Dahyot, R., Grinspun, E.: · 2010
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Exploring photobios
Deep generative stochastic networks trainable by backprop
Bengio, Y., Laufer, E., Alain, G., Yosinski, J.: · 2014
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Amortized inference in probabilistic reasoning
Gershman, S.J., Goodman, N.D.: · 2014
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Fine-grained visual comparisons with local learning
Yu, A., Grauman, K.: · 2014
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Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
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Averageexplorer: Interactive exploration and alignment of visual data collections
Zhu, J.Y., Lee, Y.J., Efros, A.A.: · 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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Kemelmacher-Shlizerman, I., Shechtman, E., Garg, R., Seitz, S.M.: · 2011
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From Learning Models of Natural Image Patches to Whole Image Restoration
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Relative attributes
Parikh, D., Grauman, K.: · 2011
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Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
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