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We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents.
Patchmatch: A randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: · 2009
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The generalized patchmatch correspondence algorithm
Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: · 2010
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Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2013
Earlier work this paper cites.
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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Learning a deep convolutional network for image super-resolution
Dong, C., Loy, C.C., He, K., Tang, X.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E.L., Chintala, S., Fergus, R., et al.: · 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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A neural algorithm of artistic style
Gatys, L.A., Ecker, A.S., Bethge, M.: · 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., et al.: · 2015
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Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: · 2016
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., Metaxas, D.: · 2016
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Plug & play generative networks: Conditional iterative generation of images in latent space
Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A., Clune, J.: · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: · 2016
Earlier work this paper cites.
Combining markov random fields and convolutional neural networks for image synthesis
Li, C., Wand, M.: · 2016
Cited alongside, same era.
Energy-based generative adversarial network
Zhao, J., Mathieu, M., LeCun, Y.: · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
Cited alongside, same era.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: · 2016
Cited alongside, same era.
Generative adversarial text to image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: · 2016
Cited alongside, same era.
Globally and Locally Consistent Image Completion
Iizuka, S., Simo-Serra, E., Ishikawa, H.: · 2017
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High-resolution image inpainting using multi-scale neural patch synthesis
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: · 2017
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Arjovsky, M., Chintala, S., Bottou, L.: · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: · 2017
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Began: Boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., Metz, L.: · 2017
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Accurate image super-resolution using very deep convolutional networks
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2016
Cited alongside, same era.
Split and match: Example-based adaptive patch sampling for unsupervised style transfer
Frigo, O., Sabater, N., Delon, J., Hellier, P.: · 2016
Cited alongside, same era.
Fast patch-based style transfer of arbitrary style
Chen, T.Q., Schmidt, M.: · 2016
Cited alongside, same era.
Image style transfer using convolutional neural networks
Gatys, L.A., Ecker, A.S., Bethge, M.: · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
Cited alongside, same era.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: · 2017
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Gans comparison without cherry-picking
: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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Semantic image inpainting with deep generative models
Yeh, R.A., Chen, C., Lim, T.Y., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: · 2017
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Shape inpainting using 3d generative adversarial network and recurrent convolutional networks
Wang, W., Huang, Q., You, S., Yang, C., Neumann, U.: · 2017
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Style transfer via texture synthesis
Elad, M., Milanfar, P.: · 2017
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High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: · 2017
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Globally and locally consistent image completion
Iizuka, S., Simo-Serra, E., Ishikawa, H.: · 2017
Closest in time.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: · 2018
Closest in time.