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pro vyhledávání: '"Varno, Farshid"'
Autor:
Varno, Farshid, Saghayi, Marzie, Sevyeri, Laya Rafiee, Gupta, Sharut, Matwin, Stan, Havaei, Mohammad
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communicated to a central hub for aggregation. While FL is an appealing decent
Externí odkaz:
http://arxiv.org/abs/2204.13170
Training Deep Neural Networks (DNNs) is still highly time-consuming and compute-intensive. It has been shown that adapting a pretrained model may significantly accelerate this process. With a focus on classification, we show that current fine-tuning
Externí odkaz:
http://arxiv.org/abs/2007.01388
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples. Prevailing fine-tuning methods could potentially contaminate pre-trained features by comparably high energy random n
Externí odkaz:
http://arxiv.org/abs/1905.10698
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