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We present two new metrics for evaluating generative models in the class-conditional image generation setting.
Journal of Multivariate Analysis 12
Dowson, D.C., Landau, B.V.: The fréchet distance between multivariate normal distributions · 1982
Earlier work this paper cites.
URL http://www.cs.toronto.edu/~kriz/cifar.html
Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (canadian institute for advanced research) (2010) · 2010
Earlier work this paper cites.
URL http://yann.lecun.com/exdb/mnist/
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010) · 2010
Earlier work this paper cites.
In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, p. 2672–2680. MIT Press, Cambridge, MA, USA (2014)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets · 2014
Earlier work this paper cites.
arXiv preprint arXiv:1411.1784 (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets · 2014
Earlier work this paper cites.
arXiv preprint arXiv:1409.1556 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition · 2014
Earlier work this paper cites.
International journal of computer vision 115
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge · 2015
Earlier work this paper cites.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2818–2826 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision · 2015
Earlier work this paper cites.
In: Advances in neural information processing systems, pp. 2172–2180 (2016)
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: Interpretable representation learning by information maximizing generative adversarial nets · 2016
Earlier work this paper cites.
arXiv preprint arXiv:1606.01583 (2016)
Odena, A.: Semi-supervised learning with generative adversarial networks · 2016
Earlier work this paper cites.
In: Advances in neural information processing systems, pp. 2234–2242 (2016)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans · 2016
Cited alongside, same era.
In: D. Precup, Y.W. Teh (eds.) Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 70, pp. 214–223. PMLR, International Convention Centre, Sydney, Australia (2017)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks · 2017
Cited alongside, same era.
In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, p. 5769–5779. Curran Associates Inc., Red Hook, NY, USA (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein gans · 2017
Cited alongside, same era.
In: Advances in neural information processing systems, pp. 6626–6637 (2017)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium · 2017
Cited alongside, same era.
In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1219–1228 (2018)
Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs · 2018
Later among the works it cites.
In: International Conference on Learning Representations (2018)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation · 2018
Later among the works it cites.
arXiv preprint arXiv:1802.05637 (2018)
Miyato, T., Koyama, M.: cgans with projection discriminator · 2018
Later among the works it cites.
In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1316–1324 (2018)
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X.: Attngan: Fine-grained text to image generation with attentional generative adversarial networks · 2018
Later among the works it cites.
In: International Conference on Learning Representations (2019)
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis · 2019
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In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks · 2017
Cited alongside, same era.
In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2642–2651. JMLR. org (2017)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier gans · 2017
Cited alongside, same era.
In: Proceedings of the IEEE international conference on computer vision, pp. 5907–5915 (2017)
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks · 2017
Cited alongside, same era.
In: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232 (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks · 2017
Cited alongside, same era.
arXiv preprint arXiv:1801.01401 (2018)
Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans · 2018
Cited alongside, same era.
In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189 (2018)
Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation · 2018
Cited alongside, same era.
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks · 2019
Later among the works it cites.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN · 2019
Later among the works it cites.
In: Advances in Neural Information Processing Systems, pp. 12268–12279 (2019)
Ravuri, S., Vinyals, O.: Classification accuracy score for conditional generative models · 2019
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6490–6499 (2019)
Singh, K.K., Ojha, U., Lee, Y.J.: Finegan: Unsupervised hierarchical disentanglement for fine-grained object generation and discovery · 2019
Later among the works it cites.