1 |
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3,
|
2 |
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten. Densely connected convolutional networks. arXiv preprint arXiv:1608.06993, 2016
|
3 |
J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016.
|
4 |
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), 2015.
|
5 |
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Aitken, A. Te- jani, J. Totz, Z. Wang, and W. Shi. Photo-realistic single image super-resolution using a generative adversarial network. In CVPR, 2017
|
6 |
S. Iizuka, E. Simo-Serra and H. Ishikawa, "Globally and locally consistent image completion," ACM Transactions on Graphics, Vol. 36, No. 4, 2017.
|
7 |
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, "Generative Adversarial Nets," International Conference on Neural Information Processing Systems, pp. 2672-2680, 2014.
|
8 |
G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, Vol. 313, pp. 504-507, 2006.
DOI
|
9 |
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell and A.A. Efros, "Context Encoders: Feature Learning by Inpainting," IEEE International Conference on Computer Vision and Pattern Recognition, pp.2536-2544, 2016.
|
10 |
O. Ronneberger, P. Fischer, and T. Brox. "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015.
|
11 |
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros, "What makes paris look like paris?," ACM Transactions on Graphics, Vol. 31, No. 4, 2012.
|
12 |
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar and C. L. Zitnick, "Microsoft COCO: common objects in context," European Conference on Computer Vision, pp. 740-755, 2014.
|