238
X. Qi et al.
6. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In:
International Conference on Machine Learning, pp. 214–223 (2017)
7. Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved training of Wasserstein GANs. In:
Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
8. Guo, R., Shi, X., Zhu, Y., et al.: Super-resolution reconstruction of astronomical images using
time-scale adaptive normalized convolution. Chin. J. Aeronaut. 31(8), 1752–1763 (2018)
9. Xue, Y., Xu, T., Zhang, H., et al.: SegAN: adversarial network with multi-scale L 1 loss for
medical image segmentation. Neuroinformatics 16, 383–392 (2018)
10. Zhang, Z., Li, F., Zhao, M., et al.: Robust neighborhood preserving projection by nuclear/L2,
1-norm regularization for image feature extraction. IEEE Trans. Image Process. 26(4), 1607–
1622 (2017)
11. Qi, G.: Loss-sensitive generative adversarial networks on Lipschitz densities. Comput. Vis.
Pattern Recogn. arXiv (2017)
12. Berg, A., Ahlberg, J., Felsberg, M.: A thermal object tracking benchmark. In: 2015 12th IEEE
International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE
(2015)