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Training deep neural networks requires gradient estimation from data batches to update parameters.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Scaling distributed machine learning with the parameter server
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Inceptionism: Going deeper into neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Regression model fitting under differential privacy and model inversion attack
Y. Wang, C. Si, and X. Wu · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Firecaffe: Near-linear acceleration of deep neural network training on compute clusters
F. N. Iandola, M. W. Moskewicz, K. Ashraf, and K. Keutzer · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Visualizing deep convolutional neural networks using natural pre-images
A. Mahendran and A. Vedaldi · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
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Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Deep models under the GAN: Information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Privacy-preserving deep learning: Revisited and enhanced
T. Le, Y. Aono, T. Hayashi, et al · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Learning generative convnets via multi-grid modeling and sampling
R. Gao, Y. Lu, J. Zhou, S.-C. Zhu, and Y. Nian Wu · 2018
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Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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Mixed precision training
P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, and H. Wu · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi · 2019
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Adversarial neural network inversion via auxiliary knowledge alignment
Z. Yang, E.-C. Chang, and Z. Liang · 2019
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Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2019
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Deep leakage from gradients
L. Zhu, Z. Liu, and S. Han · 2019
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ZeroQ: A novel zero shot quantization framework
Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer · 2020
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A simple framework for contrastive learning of visual representations
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Federated learning for ultra-reliable low-latency V2V communications
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah · 2018
Cited alongside, same era.
Context encoding for semantic segmentation
H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
Cited alongside, same era.
Towards federated learning at scale: System design
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, H. B. McMahan, et al · 2019
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Data-free learning of student networks
H. Chen, Y. Wang, C. Xu, Z. Yang, C. Liu, B. Shi, C. Xu, C. Xu, and Q. Tian · 2019
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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2020
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Inverting gradients–How easy is it to break privacy in federated learning?
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller · 2020
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Inverting Gradients Github Source Code
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller · 2020
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Your classifier is secretly an energy based model and you should treat it like one
W. Grathwohl, K.-C. Wang, J.-H. Jacobsen, D. Duvenaud, M. Norouzi, and K. Swersky · 2020
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The knowledge within: Methods for data-free model compression
M. Haroush, I. Hubara, E. Hoffer, and D. Soudry · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Analyzing and improving the image quality of StyleGAN
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
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RANSAC-Flow: Generic two-stage image alignment
X. Shen, F. Darmon, A. A. Efros, and M. Aubry · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Y. Zhang, R. Jia, H. Pei, W. Wang, B. Li, and D. Song · 2020
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iDLG: Improved deep leakage from gradients
B. Zhao, K. R. Mopuri, and H. Bilen · 2020
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Deep Leakage From Gradients Github Source Code
L. Zhu, Z. Liu, and S. Han · 2020
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