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Face hallucination, which is the task of generating a high-resolution face image from a low-resolution input image, is a well-studied problem that is useful in widespread application areas.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Effective unconstrained face recognition by combining multiple descriptors and learned background statistics
Wolf, L., Hassner, T., Taigman, Y.: · 1990
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Eigenfaces for recognition
Turk, M., Pentland, A.: · 1991
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Improving resolution by image registration
Irani, M., Peleg, S.: · 1991
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Hallucinating faces
Baker, S., Kanade, T.: · 2000
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Image quality assessment based on a degradation model
Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: · 2000
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Example-based super-resolution
Freeman, W.T., Jones, T.R., Pasztor, E.C.: · 2002
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Multi-scale structural similarity for image quality assessment
Wang, Z., Simoncelli, E.P., Bovik, A.C.: · 2003
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Overview of the face recognition grand challenge
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: · 2005
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An information fidelity criterion for image quality assessment using natural scene statistics
Sheikh, H.R., Bovik, A.C., De Veciana, G.: · 2005
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Reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R.: · 2006
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Face hallucination: Theory and practice
Liu, C., Shum, H.Y., Freeman, W.T.: · 2007
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Image upsampling via imposed edge statistics
Fattal, R.: · 2007
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Image super-resolution using gradient profile prior
Sun, J., Sun, J., Xu, Z., Shum, H.Y.: · 2008
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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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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: · 2008
Cited alongside, same era.
Super-resolution from a single image
Glasner, D., Bagon, S., Irani, M.: · 2009
Cited alongside, same era.
Natural image denoising with convolutional networks
Jain, V., Seung, S.: · 2009
Cited alongside, same era.
Single-image super-resolution using sparse regression and natural image prior
Kim, K.I., Kwon, Y.: · 2010
Cited alongside, same era.
Image super-resolution via sparse representation
Yang, J., Wright, J., Huang, T.S., Ma, Y.: · 2010
Cited alongside, same era.
Hallucinating face by position-patch
Ma, X., Zhang, J., Qi, C.: · 2010
Cited alongside, same era.
Single-image super-resolution: A benchmark
Yang, C.Y., Ma, C., Yang, M.H.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Deepface: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: · 2014
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Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
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Deep convolutional neural network for image deconvolution
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Deconvolutional networks
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: · 2010
Cited alongside, same era.
Multimodal deep learning
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: · 2011
Cited alongside, same era.
Face detection, pose estimation, and landmark localization in the wild
Zhu, X., Ramanan, D.: · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Cited alongside, same era.
Image denoising: Can plain neural networks compete with BM3D?
Burger, H.C., Schuler, C.J., Harmeling, S.: · 2012
Cited alongside, same era.
Image denoising and inpainting with deep neural networks
Xie, J., Xu, L., Chen, E.: · 2012
Cited alongside, same era.
Xu, L., Ren, J.S., Liu, C., Jia, J.: · 2014
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Image super-resolution using deep convolutional networks
Dong, C., Loy, C.C., He, K., Tang, X.: · 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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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Self-tuned deep super resolution
Wang, Z., Yang, Y., Wang, Z., Chang, S., Han, W., Yang, J., Huang, T.S.: · 2015
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Learning face hallucination in the wild
Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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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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Unsupervised learning of visual structure using predictive generative networks
Lotter, W., Kreiman, G., Cox, D.: · 2015
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