[2] Q. Bai, C. Yang, Y. Xu, X. Liu, Y. Yang, and Y. Shen. Glead: Improving gans with a generator-leading task. IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[3] E. Betzalel, C. Penso, A. Navon, and E. Fetaya. A study on the evaluation of generative models. arXiv preprint arXiv:2206.10935, 2022.
[4] M. Binkowski, D. J. Sutherland, M. Arbel, and A. Gretton. Demystifying MMD GANs. In Int. Conf. Learn. Represent., 2018.
[5] A. Brock, J. Donahue, and K. Simonyan. Large scale gan training for high fidelity natural image synthesis. In Int. Conf. Learn. Represent., 2019.
[6] M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural Inform. Process. Syst., pages 9912-9924, 2020.
[7] A. Casanova, M. Careil, J. Verbeek, M. Drozdzal, and A. Romero-Soriano. Instance-conditioned gan. In Adv. Neural Inform. Process. Syst., 2021.
[8] X. Chen, H. Fan, R. Girshick, and K. He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020.
[9] X. Chen, S. Xie, and K. He. An empirical study of training self-supervised vision transformers. In Int. Conf. Comput. Vis., pages 9640-9649, 2021.
[10] C. Cortes, M. Mohri, and A. Rostamizadeh. Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research, 13:795-828, 2012.
[11] N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola. On kernel-target alignment. Adv. Neural Inform. Process. Syst., 2001.
[12] M. Davari, S. Horoi, A. Natik, G. Lajoie, G. Wolf, and E. Belilovsky. Reliability of cka as a similarity measure in deep learning. arXiv preprint arXiv:2210.16156, 2022.
[13] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009.
[14] P. Dhariwal and A. Nichol. Diffusion models beat gans on image synthesis. In Adv. Neural Inform. Process. Syst., pages 8780-8794, 2021.
[15] X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun. Repvgg: Making vgg-style convnets great again. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13733-13742, 2021.
[16] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Int. Conf. Learn. Represent., 2020.
[17] D. Dowson and B. Landau. The fréchet distance between multivariate normal distributions. Journal of multivariate analysis, 12(3):450-455, 1982.
[18] R. Ganz and M. Elad. BIGRec: Boosting image generation via a robust classifier. Transactions on Machine Learning Research, 2022. ISSN 2835-8856.
[19] S. Gao, P. Zhou, M.-M. Cheng, and S. Yan. Masked diffusion transformer is a strong image synthesizer. Int. Conf. Comput. Vis., 2023.
[20] A. Gretton, O. Bousquet, A. Smola, and B. Scholkopf. Measuring statistical dependence with hilbert-schmidt norms. In International conference on algorithmic learning theory, pages 63-77, 2005.
[21] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 770-778, 2016.
[22] 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 Adv. Neural Inform. Process. Syst., 2017.
[23] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Adv. Neural Inform. Process. Syst., pages 6840-6851, 2020.
[24] L. Huang, D. Chen, Y. Liu, Y. Shen, D. Zhao, and J. Zhou.Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023.