November 23, 2022
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.
Publisher
The International Symposium on Information Theory and Its Applications (ISITA)
October 01, 2023
Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh, Xi Liu
October 01, 2023
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Shuangzhi Li, Zhijie Wang, Felix Xu, Qing Guo, Xingyu Li, Lei Ma
September 22, 2023
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Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Ellen Tan, Yossef (Yossi) Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Defossez, Jade Copet, Faisal Azhar, Hugo Touvron, Gabriel Synnaeve, Louis Martin, Nicolas Usunier, Thomas Scialom
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June 26, 2023
Vivien Cabannes, Bobak Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti
June 26, 2023
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