[20] X. Chen, H. Fan, R. Girshick, and K. He. Improved baselines with momentum contrastive learning. arXiv:2003.04297, 2020.
[21] R. Child. Very deep VAEs generalize autoregressive models and can outperform them on images. In International Conference on Learning Representations, 2021.
[22] M. J. Chong and D. Forsyth. Effectively unbiased FID and inception score and where to find them. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6070-6079, 2020.
[23] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009.
[24] E. L. Denton, S. Chintala, a. szlam, and R. Fergus. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. In Advances in Neural Information Processing Systems, volume 28, 2015.
[25] P. Dhariwal and A. Nichol. Diffusion models beat GANs on image synthesis. In Advances in Neural Information Processing Systems, volume 34, pages 8780-8794, 2021.
[26] L. Dinh, J. Sohl-Dickstein, and S. Bengio. Density estimation using Real NVP. In International Conference on Learning Representations, 2017.
[27] C. Donahue, A. Balsubramani, J. McAuley, and Z. C. Lipton. Semantically decomposing the latent spaces of generative adversarial networks. In International Conference on Learning Representations, 2018.
[28] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Jakob Uszkoreit, and N. Houlsb. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021.
[29] B. D. Douglas, P. J. Ewell, and M. Brauer. Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA. PLOS ONE, 18(3):e0279720, 2023.
[30] L. Ericsson, H. Gouk, and T. M. Hospedales. Why do self-supervised models transfer? Investigating the impact of invariance on downstream tasks. arXiv:2111.11398, 2021.
[31] P. Eyal, R. David, G. Andrew, E. Zak, and D. Ekaterina. Data quality of platforms and panels for online behavioral research. Behavior Research Methods, pages 1-20, 2021.
[32] J. Feydy. Geometric data analysis, beyond convolutions. PhD thesis, Université Paris-Saclay Gif-sur-Yvette, France, 2020.
[33] D. Friedman and A. B. Dieng. The Vendi Score: A diversity evaluation metric for machine learning. arXiv:2210.02410, 2022.
[34] S. Fu, N. Tamir, S. Sundaram, L. Chai, R. Zhang, T. Dekel, and P. Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. arXiv:2306.09344, 2023.
[35] S. Y. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, E. Orgad, R. Entezari, S. Daras, Giannis adn Pratt, V. Ramanujan, Y. Bitton, K. Marathe, S. Mussmann, R. Vencu, M. Cherti, R. Krishna, P. W. Koh, O. Saukh, A. Ratner, S. Song, H. Hajishirzi, A. Farhadi, R. Beaumont, S. Oh, A. Dimakis, J. Jitsev, Y. Carmon, V. Shankar, and L. Schmidt. DataComp: In search of the next generation of multimodal datasets. arXiv:2304.14108, 2023.
[36] R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations, 2019.
[37] J. Gildenblat and contributors. Pytorch library for CAM methods. https://github.com/jacobgil/pytorch-grad-cam, 2021.
[38] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139-144, 2020.
[39] A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola. A kernel two-sample test. The Journal of Machine Learning Research, 13(1):723-773, 2012.