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This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
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Federated Unsupervised Representation Learning
Zhang, F.; Kuang, K.; You, Z.; Shen, T.; Xiao, J.; Zhang, Y.; Wu, C.; Zhuang, Y.; and Li, X. 2020 · 2010
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Adam: A method for stochastic optimization
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Fitnets: Hints for thin deep nets
Romero, A.; Ballas, N.; Kahou, S. E.; Chassang, A.; Gatta, C.; and Bengio, Y. 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Tiny imagenet visual recognition challenge
Le, Y.; and Yang, X. 2015 · 2015
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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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Federated learning: Strategies for improving communication efficiency
Konecnuy, J.; McMahan, H. B.; Yu, F. X.; Richtárik, P.; Suresh, A. T.; and Bacon, D. 2016 · 2016
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Relational knowledge distillation
Park, W.; Kim, D.; Lu, Y.; and Cho, M. 2019 · 2019
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Amalgamating knowledge towards comprehensive classification
Shen, C.; Wang, X.; Song, J.; Sun, L.; and Song, M. 2019 · 2019
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Student becoming the master: Knowledge amalgamation for joint scene parsing, depth estimation, and more
Ye, J.; Ji, Y.; Wang, X.; Ou, K.; Tao, D.; and Song, M. 2019 · 2019
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Deep geometric knowledge distillation with graphs
Lassance, C.; Bontonou, M.; Hacene, G. B.; Gripon, V.; Tang, J.; and Ortega, A. 2020 · 2020
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Colorful image colorization
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Communication-efficient learning of deep networks from decentralized data
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Kim, J.; Park, S.; and Kwak, N. 2018 · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
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Smith, V.; Chiang, C.-K.; Sanjabi, M.; and Talwalkar, A. 2018 · 2018
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Unsupervised feature learning via non-parametric instance-level discrimination
Wu, Z.; Xiong, Y.; Yu, S.; and Lin, D. 2018 · 2018
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Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
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Towards federated unsupervised representation learning
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T.; and Isola, P. 2020 · 2020
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An empirical study of training self-supervised vision transformers
Chen, X.; Xie, S.; and He, K. 2021 · 2021
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Seed: Self-supervised distillation for visual representation
Fang, Z.; Wang, J.; Wang, L.; Zhang, L.; Yang, Y.; and Liu, Z. 2021 · 2021
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Ensemble Attention Distillation for Privacy-Preserving Federated Learning
Gong, X.; Sharma, A.; Karanam, S.; Wu, Z.; Chen, T.; Doermann, D.; and Innanje, A. 2021 · 2021
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FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
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Barlow twins: Self-supervised learning via redundancy reduction
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Collaborative Unsupervised Visual Representation Learning from Decentralized Data
Zhuang, W.; Gan, X.; Wen, Y.; Zhang, S.; and Yi, S. 2021 · 2021
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