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Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2013
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Privacy in pharmacogenetics: An { \{ End-to-End } \} case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T · 2014
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Earlier work this paper cites.
Large batch training of convolutional networks
You, Y., Gitman, I., and Ginsburg, B · 2017
Earlier work this paper cites.
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M · 2018
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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
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Generative image inpainting with contextual attention
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T. S · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., and Jégou, H · 2019
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Chen, X. and He, K · 2020
Cited alongside, same era.
Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Reconstructing training data with informed adversaries
Balle, B., Cherubin, G., and Hayes, J · 2022
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Bardes, A., Ponce, J., and LeCun, Y · 2022
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Bounding training data reconstruction in private (deep) learning
Guo, C., Karrer, B., Chaudhuri, K., and van der Maaten, L · 2022
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Feldman, V · 2020
Cited alongside, same era.
Bootstrap your own latent: A new approach to self-supervised learning
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 · 2020
Cited alongside, same era.
Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., and Joulin, J. M. P. B. A · 2021
Cited alongside, same era.
On the importance of difficulty calibration in membership inference attacks
Watson, L., Guo, C., Cormode, G., and Sablayrolles, A · 2021
Cited alongside, same era.
Enhanced membership inference attacks against machine learning models
Ye, J., Maddi, A., Murakonda, S. K., Bindschaedler, V., and Shokri, R · 2021
Cited alongside, same era.
Guillotine regularization: Improving deep networks generalization by removing their head, 2022a
Bordes, F., Balestriero, R., Garrido, Q., Bardes, A., and Vincent, P
Cited in the paper.
Are attribute inference attacks just imputation?
Jayaraman, B. and Evans, D · 2022
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Mehnaz, S., Dibbo, S. V., Kabir, E., Li, N., and Bertino, E · 2022
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Simclrt: A simple framework for contrastive learning of rumor tracking
Zeng, H. and Cui, X · 2022
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Towards democratizing joint-embedding self-supervised learning, 2023
Bordes, F., Balestriero, R., and Vincent, P · 2023
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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