參考文獻 |
[1] M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv:1411.1784v1.
[2] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” arXiv:1611.07004v3.
[3] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” arXiv:1505.04597v1.
[4] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv:1406.2661v1.
[5] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” arXiv:1710.07035v1.
[6] A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier GANs,” arXiv:1610.09585v4.
[7] A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv:1511.06434v2.
[8] L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,” arXiv:1508.06576v2.
[9] L. A. Gatys, A. S. Ecker, and M. Bethge, “Image style transfer using convolutional neural networks,” in Proc. of the IEEE Conf. on CVPR 2016, Las Vegas, NV, Jun.27-30, 2016, pp.2414-2423.
[10] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556v6.
[11] J. Johnson, A. Alahi, and F.-F. Li, “Perceptual losses for real-time style transfer and super-resolution,” arXiv:1603.08155v1.
[12] T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” arXiv:1812.04948v3.
[13] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” arXiv:1912.04958v2.
[14] X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” arXiv:1703.06868v2.
[15] A. Karnewar, and O. Wang, “MSG-GAN: Multi-Scale gradients for generative adversarial networks,” arXiv:1903.06048v4.
[16] Y. Pang, J. Lin, T. Qin, and Z. Chen, “Image-to-image translation: Methods and applications,” arXiv:2101.08629v2.
[17] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” arXiv:1411.4038v2.
[18] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” arXiv:1703.10593v7.
[19] L. Kong, C. Lian, D. Huang, Z. Li, Y. Hu, and Q. Zhou, “Breaking the dilemma of medical image-to-image translation,” arXiv:2110.06465v2.
[20] A. Antoniou, A. Storkey, and H. Edwards, “Data augmentation generative adversarial networks,” arXiv:1711.04340v3.
[21] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv:1512.03385v1.
[22] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” arXiv:1608.06993v5.
[23] M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv:1701.07875v3.
[24] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of Wasserstein GANs,” arXiv:1704.00028v3.
[25] T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He, “AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks,” arXiv:1711.10485v1.
[26] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.-N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” arXiv:1706.03762v5.
[27] H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” arXiv:1805.08318v2.
[28] J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-excitation networks,” arXiv:1709.01507v4.
[29] S. Woo, J. Park, J.-Y. Lee, and I. Kweon, “CBAM: convolutional block attention module,” arXiv:1807.06521v2.
[30] J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” arXiv:1809.02983v4.
[31] J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman, “Toward multimodal image-to-image translation,” arXiv:1711.11586v4.
[32] S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” arXiv:1502.03167v3.
[33] D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Instance normalization: the missing ingredient for fast stylization,” arXiv:1607.08022v3.
[34] V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. of ICML Conf., Haifa, Israel, Jun.21-24, 2010, pp.807-814.
[35] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. of ICML Conf., Atlanta, GA, Jun.16-21, 2013, pp.1-6.
[36] X. Mao, Q. Li, H. Xie, R. Y.K. Lau, Z. Wang, and S. P. Smolley, “Least squares generative adversarial networks,” arXiv:1611.04076v3.
[37] D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” arXiv:1412.6980v9.
[38] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local Nash equilibrium,” arXiv:1706.08500v6.
[39] M. Tan, and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” arXiv:1905.11946v5. |