[1] Balle, B., Cherubin, G., and Hayes, J. Reconstructing training data with informed adversaries. arXiv preprint arXiv:2201.04845, 2022.
[2] Bardes, A., Ponce, J., and LeCun, Y. Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. URL https://openreview.net/forum?id=xm6YD62D1Ub.
[3] Bordes, F., Balestriero, R., Garrido, Q., Bardes, A., and Vincent, P. Guillotine regularization: Improving deep networks generalization by removing their head, 2022. URL https://arxiv.org/abs/2206.13378.
[4] Bordes, F., Balestriero, R., and Vincent, P. High fidelity visualization of what your self-supervised representation knows about. Transactions on Machine Learning Research, 2022. URL https://openreview.net/forum?id=urfWb7VjmL.
[5] Bordes, F., Balestriero, R., and Vincent, P. Towards democratizing joint-embedding self-supervised learning, 2023. URL https://arxiv.org/abs/2303.01986.
[6] Carlini, N., Liu, C., Erlingsson, U., Kos, J., and Song, D. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pp. 267-284, 2019.
[7] Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pp. 2633-2650, 2021.
[8] Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E. Extracting training data from diffusion models. arXiv preprint arXiv:2301.13188, 2023.
[9] Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, 2020.
[10] Caron, M., Touvron, H., Misra, I., Jegou, H., and Joulin, J. M. P. B. A. Emerging properties in self-supervised vision transformers. In ICCV, 2021.
[11] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E. A simple framework for contrastive learning of visual representations. In ICML, 2020.
[12] Chen, X. and He, K. Exploring simple siamese representation learning. In CVPR, 2020.
[13] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. IEEE, 2009.
[14] Dwork, C. and Roth, A. The algorithmic foundations of differential privacy. Theoretical Computer Science, 9(3-4):211-407, 2013.
[15] Dwork, C., McSherry, F., Nissim, K., and Smith, A. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography, pp. 265-284. Springer, 2006.
[16] Feldman, V. Does learning require memorization? a short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pp. 954–959, 2020.
[17] Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T. Privacy in pharmacogenetics: An {End-to-End} case study of personalized warfarin dosing. In 23rd USENIX Security Symposium (USENIX Security 14), pp. 17–32, 2014.
[18] Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M. Bootstrap your own latent: A new approach to self-supervised learning. In NeurlPS, 2020.