M. B. Sariyildiz, K. Alahari, D. Larlus, and Y. Kalantidis. Fake it till you make it: Learning transferable representations from synthetic imagenet clones. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
I. Shumailov, Z. Shumaylov, Y. Zhao, Y. Gal, N. Papernot, and R. Anderson. The curse of recursion: Training on generated data makes models forget, 2023.
A. Singh, R. Hu, V. Goswami, G. Couairon, W. Galuba, M. Rohrbach, and D. Kiela. FLAVA: A foundational language and vision alignment model. In CVPR, 2022.
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein. Diffusion art or digital forgery? investigating data replication in diffusion models. arXiv preprint arXiv:2212.03860, 2022.
O. Struckmeier, K. Tiwari, and V. Kyrki. Autoencoding slow representations for semi-supervised data-efficient regression. Machine Learning, pages 1-19, 2023.
A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. Chaplot, O. Maksymets, A. Gokaslan, V. Vondrus, S. Dharur, F. Meier, W. Galuba, A. Chang, Z. Kira, V. Koltun, J. Malik, M. Savva, and D. Batra. Habitat 2.0: Training home assistants to rearrange their habitat, 2021.
K. S. Tai, P. Bailis, and G. Valiant. Equivariant transformer networks. In K. Chaudhuri and R. Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 6086-6095. PMLR, 2019. URL http://proceedings.mlr.press/v97/tai19a.html.
T. Thrush, R. Jiang, M. Bartolo, A. Singh, A. Williams, D. Kiela, and C. Ross. Winoground: Probing vision and language models for visio-linguistic compositionality. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5238-5248, 2022.
B. Trabucco, K. Doherty, M. Gurinas, and R. Salakhutdinov. Effective data augmentation with diffusion models, 2023.
M. Trager, P. Perera, L. Zancato, A. Achille, P. Bhatia, and S. Soatto. Linear spaces of meanings: Compositional structures in vision-language models, 2023.
A. Ushio, L. Espinosa Anke, S. Schockaert, and J. Camacho-Collados. BERT is to NLP what AlexNet is to CV: Can pre-trained language models identify analogies? In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, Aug. 2021. Association for Computational Linguistics.
T. Wang, K. Lin, L. Li, C.-C. Lin, Z. Yang, H. Zhang, Z. Liu, and L. Wang. Equivariant similarity for vision-language foundation models, 2023.
L. Weng. From autoencoder to beta-vae. *lilianweng.github.io*, 2018. URL https://lilianweng.github.io/posts/2018-08-12-vae/.
R. Wightman, H. Touvron, and H. Jégou. Resnet strikes back: An improved training procedure in timm, 2021.
T. Wolf, L. Debut, V. Sanh, J. Chaumont, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online, Oct. 2020. Association for Computational Linguistics. URL https://www.aclweb.org/anthology/2020.emnlp-demos .6.
K. Xiao, L. Engstrom, A. Ilyas, and A. Madry. Noise or signal: The role of image backgrounds in object recognition. arXiv preprint arXiv:2006.09994, 2020.