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Creators/Authors contains: "Nguyen, Cuong N"

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  1. ; ; ; ; ; ; (, PMLR: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence)
    We consider transferability estimation, the prob- lem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computa- tionally efficient approaches that estimate trans- ferability based on the negative regularized mean squared error of a linear regression model. We prove novel theoretical results connecting our ap- proaches to the actual transferability of the optimal target models obtained from the transfer learning process. Despite their simplicity, our approaches significantly outperform existing state-of-the-art regression transferability estimators in both accu- racy and efficiency. On two large-scale keypoint re- gression benchmarks, our approaches yield 12% to 36% better results on average while being at least 27% faster than previous state-of-the-art methods. 
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