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In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited.
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Imagenet: A large-scale hierarchical image database
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A fully homomorphic encryption scheme
Gentry, C. 2009 · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
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ML confidential: Machine learning on encrypted data
Graepel, T.; Lauter, K.; and Naehrig, M. 2012 · 2012
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(Leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z.; Gentry, C.; and Vaikuntanathan, V. 2014 · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Gilad-Bachrach, R.; Dowlin, N.; Laine, K.; Lauter, K.; Naehrig, M.; and Wernsing, J. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Privacy preserving multi-party machine learning with homomorphic encryption
Takabi, H.; Hesamifard, E.; and Ghasemi, M. 2016 · 2016
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A survey of transfer learning
Weiss, K.; Khoshgoftaar, T. M.; and Wang, D. 2016 · 2016
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Homomorphic encryption for arithmetic of approximate numbers
Cheon, J. H.; Kim, A.; Kim, M.; and Song, Y. 2017 · 2017
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Bootstrapping for approximate homomorphic encryption
Cheon, J. H.; Han, K.; Kim, A.; Kim, M.; and Song, Y. 2018 · 2018
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
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A low-depth homomorphic circuit for logistic regression model training
Crockett, E. 2020 · 2020
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Face Mask Detection Dataset
Larxel. 2020 · 2020
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HomoPAI: A Secure Collaborative Machine Learning Platform based on Homomorphic Encryption
Li, Q.; Huang, Z.; Lu, W.-j.; Hong, C.; Qu, H.; He, H.; and Zhang, W. 2020 · 2020
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Glyph: Fast and accurately training deep neural networks on encrypted data
Lou, Q.; Feng, B.; Charles Fox, G.; and Jiang, L. 2020 · 2020
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Practical FHE parameters against lattice attacks
Cheon, J. H.; Son, Y.; and Yhee, D. 2021 · 2021
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Faster cryptonets: Leveraging sparsity for real-world encrypted inference
Chou, E.; Beal, J.; Levy, D.; Yeung, S.; Haque, A.; and Fei-Fei, L. 2018 · 2018
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Privacy-preserving machine learning as a service
Hesamifard, E.; Takabi, H.; Ghasemi, M.; and Wright, R. N. 2018 · 2018
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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P.; Rosendahl, C.; and Kittler, H. 2018 · 2018
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Logistic regression on homomorphic encrypted data at scale
Han, K.; Hong, S.; Cheon, J. H.; and Park, D. 2019 · 2019
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Towards deep neural network training on encrypted data
Nandakumar, K.; Ratha, N.; Pankanti, S.; and Halevi, S. 2019 · 2019
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Training data-efficient image transformers & distillation through attention
Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; and Jégou, H. 2021 · 2021
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The Rise of Fully Homomorphic Encryption: Often called the Holy Grail of cryptography, commercial FHE is near
Creeger, M. 2022 · 2022
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HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Lee, S.; Lee, G.; Kim, J. W.; Shin, J.; and Lee, M.-K. 2023 · 2023
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MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Yang, J.; Shi, R.; Wei, D.; Liu, Z.; Zhao, L.; Ke, B.; Pfister, H.; and Ni, B. 2023 · 2023
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