[40] J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko. Bootstrap your own latent - a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, volume 33, pages 21271-21284, 2020.
[41] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville. Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems, volume 30, 2017.
[42] J. Han, H. Choi, Y. Choi, J. Kim, J.-W. Ha, and J. Choi. Rarity score: A new metric to evaluate the uncommonness of synthesized images. In The Eleventh International Conference on Learning Representations, 2022.
[43] L. Hazami, R. Mama, and R. Thurairatnam. Efficient-VDVAE: Less is more. arXiv:2203.13751, 2022.
[44] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022.
[45] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[46] D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan. Augmix: A simple method to improve robustness and uncertainty under data shift. In International Conference on Learning Representations, 2020.
[47] K. Hermann, T. Chen, and S. Kornblith. The origins and prevalence of texture bias in convolutional neural networks. In Advances in Neural Information Processing Systems, volume 33, pages 19000-19015, 2020.
[48] 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. In Advances in Neural Information Processing Systems, volume 30, 2017.
[49] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, volume 33, pages 6840-6851, 2020.
[50] X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie. Stacked generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 2017.
[51] G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt. Openclip, July 2021.
[52] A. Jacot, F. Gabriel, and C. Hongler. Neural tangent kernel: Convergence and generalization in neural networks. In Advances in Neural Information Processing Systems, volume 31, 2018.
[53] M. Jiralerspong, A. J. Bose, and G. Gidel. Feature likelihood score: Evaluating generalization of generative models using samples. arXiv:2302.04440, 2023.
[54] M. Kang, W. Shim, M. Cho, and J. Park. Rebooting ACGAN: Auxiliary classifier GANs with stable training. In Advances in Neural Information Processing Systems, volume 34, pages 23505-23518, 2021.
[55] M. Kang, J. Shin, and J. Park. StudioGAN: A taxonomy and benchmark of GANs for image synthesis. arXiv:2206.09479, 2022.
[56] M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park. Scaling up GANs for text-to-image synthesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023.
[57] T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila. Training generative adversarial networks with limited data. In Advances in Neural Information Processing Systems, volume 33, pages 12104-12114, 2020.
[58] T. Karras, S. Laine, and T. Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4401-4410, 2019.
[59] M. Khayatkhoei and W. AbdAlmageed. Emergent asymmetry of precision and recall for measuring fidelity and diversity of generative models in high dimensions. arXiv:2306.09618, 2023.
[60] D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In International Conference on Learning Representations, 2014.