I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014, pp. 2672–2680.
2.2 Laplacian Pyramid of Adversarial Networks (LAPGAN)
E. L. Denton, S. Chintala, R. Fergus et al., “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in neural information processing systems, 2015, pp. 1486–1494.
P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Transactions on communications, vol. 31, no. 4, pp. 532–540, 1983.
2.3 Deep Convolutional GAN (DCGAN)
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015.
2.4 Boundary Equibrium GAN (BEGAN)
J. Zhao, M. Mathieu, and Y. LeCun, “Energy-based generative adversarial network,” arXiv preprint arXiv:1609.03126, 2016.
2.5 Progressive GAN (ProGAN)
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196, 2017
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096, 2018
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” arXiv preprint arXiv:1812.04948, 2018
2.6 Self-Attention GAN (SAGAN)
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” arXiv preprint arXiv:1805.08318, 2018.
2.7 BigGAN
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096, 2018.
3. 損失関数
3.1 Wasserstein GAN (WGAN)
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International journal of computer vision, vol. 40, no. 2, pp. 99–121, 2000.
3.2 WGAN-GP
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein GANs,” in Advances in Neural Information Processing Systems, 2017, pp. 5767–5777.
3.3 Least Square GAN (LSGAN)
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. P. Smolley, “Least squares generative adversarial networks,” in 2017 IEEE International Conference on Computer Vision. IEEE, 2017, pp. 2813–2821.
3.4 F-GAN
S. Nowozin, B. Cseke, and R. Tomioka, “f-GAN: Training generative neural samplers using variational divergence minimization,” in Advances in neural information processing systems, 2016, pp. 271–279.
3.5 Unrolled GAN (UGAN)
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein, “Unrolled generative adversarial networks,” arXiv preprint arXiv:1611.02163, 2016.
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li, “Mode regularized generative adversarial networks,” arXiv preprint arXiv:1612.02136, 2016.
3.8 Geometric GAN
J. H. Lim and J. C. Ye, “Geometric GAN,” arXiv preprint arXiv:1705.02894, 2017.
3.9 Relativistic GAN (RGAN)
A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard GAN,” arXiv preprint arXiv:1807.00734, 2018.
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. Lanckriet, “On integral probability metrics, ϕ-divergences and binary classification,” arXiv preprint arXiv:0901.2698, 2009.
A. Müller, “Integral probability metrics and their generating classes of functions,” Advances in Applied Probability, vol. 29, no. 2, pp. 429–443, 1997.
3.10 Spectral normalization GAN (SN-GAN)
Y. Yoshida and T. Miyato, “Spectral norm regularization for improving the generalizability of deep learning,” arXiv preprint arXiv:1705.10941, 2017.
A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in Proceedings of the 34th International Conference on Machine Learning, vol. 70. JMLR, 2017, pp. 2642–2651.
T. Donchev and E. Farkhi, “Stability and euler approximation of one-sided lipschitz differential inclusions,” SIAM journal on control and optimization, vol. 36, no. 2, pp. 780–796, 1998.
L. Armijo, “Minimization of functions having lipschitz continuous first partial derivatives,” Pacific Journal of mathematics, vol. 16, no. 1, pp. 1–3, 1966.
A. Goldstein, “Optimization of lipschitz continuous functions,” Mathematical Programming, vol. 13, no. 1, pp. 14–22, 1977.
shimopino
commented
4 years ago
論文へのリンク
[arXiv:1906.01529] Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
著者・所属機関
Zhengwei Wang, Qi She, Tomas E. Ward
投稿日時（YYYY-MM-DD）
2019-06-04
1. 概要
GANの実応用には3つの課題が存在する。本論文ではこれらの課題に対して、(1)ネットワーク構造と(2)損失関数の視点からどのように解決しようとしているのか見ていく。
ここでは後で見返すように論文のリストを載せておく。
2. ネットワーク構造
2.1 Fully-Connected GAN (FCGAN)
2.2 Laplacian Pyramid of Adversarial Networks (LAPGAN)
E. L. Denton, S. Chintala, R. Fergus et al., “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in neural information processing systems, 2015, pp. 1486–1494.
P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Transactions on communications, vol. 31, no. 4, pp. 532–540, 1983.
2.3 Deep Convolutional GAN (DCGAN)
2.4 Boundary Equibrium GAN (BEGAN)
2.5 Progressive GAN (ProGAN)
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196, 2017
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096, 2018
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” arXiv preprint arXiv:1812.04948, 2018
2.6 Self-Attention GAN (SAGAN)
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” arXiv preprint arXiv:1805.08318, 2018.
2.7 BigGAN
3. 損失関数
3.1 Wasserstein GAN (WGAN)
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International journal of computer vision, vol. 40, no. 2, pp. 99–121, 2000.
3.2 WGAN-GP
3.3 Least Square GAN (LSGAN)
3.4 F-GAN
3.5 Unrolled GAN (UGAN)
3.6 Loss Sensitive GAN (LS-GAN)
3.7 Mode Regularized GAN (MRGAN)
3.8 Geometric GAN
3.9 Relativistic GAN (RGAN)
A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard GAN,” arXiv preprint arXiv:1807.00734, 2018.
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. Lanckriet, “On integral probability metrics, ϕ-divergences and binary classification,” arXiv preprint arXiv:0901.2698, 2009.
A. Müller, “Integral probability metrics and their generating classes of functions,” Advances in Applied Probability, vol. 29, no. 2, pp. 429–443, 1997.
3.10 Spectral normalization GAN (SN-GAN)
Y. Yoshida and T. Miyato, “Spectral norm regularization for improving the generalizability of deep learning,” arXiv preprint arXiv:1705.10941, 2017.
A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in Proceedings of the 34th International Conference on Machine Learning, vol. 70. JMLR, 2017, pp. 2642–2651.
T. Donchev and E. Farkhi, “Stability and euler approximation of one-sided lipschitz differential inclusions,” SIAM journal on control and optimization, vol. 36, no. 2, pp. 780–796, 1998.
L. Armijo, “Minimization of functions having lipschitz continuous first partial derivatives,” Pacific Journal of mathematics, vol. 16, no. 1, pp. 1–3, 1966.
A. Goldstein, “Optimization of lipschitz continuous functions,” Mathematical Programming, vol. 13, no. 1, pp. 14–22, 1977.