Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transfer a source model (  source task) to a target task. Recent analytical transferability metrics have been widely used for source model selection and multi-task learning. Earlier metrics does not work sufficiently well under the challenging cross-domain cross-task transfer settings, but recent OTCE score achieves a noteworthy performance using auxiliary tasks. A simplified version named OT-based NCE score sacrifices accuracy to be more efficient, but it can be further improved. Consequently ,  we propose a practical transferability metric called JC-NCE score to further improve the cross-domain cross-task transferability estimation performance, which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with  considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy  . Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the OT-based NCE score with about 7\% and 12\% gains, respectively .
Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model ( or source task) to a target task. Recent analytical transferability metrics have been widely used for source model selection and multi-task learning. A major challenge is how to make transfereability estimation robust under the cross-domain cross-task settings. The recently proposed OTCE score solves this problem by considering both domain and task differences, with the help of transfer experiences on auxiliary tasks , which causes an efficiency overhead. In this work, we propose a practical transferability metric called JC-NCE score that dramatically improves the robustness of the task difference estimation in OTCE, thus removing the need for auxiliary tasks . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with a ground cost considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy of the matched labels . Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the auxiliary-task free version of OTCE for 7\% and 12\% , respectively, and is also more robust than other existing transferability metrics on average .
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Earlier metrics does not work sufficiently well under the challenging
A major challenge is how to make transfereability estimation robust under the
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transfer settings, but recent OTCE score achieves a noteworthy performance using auxiliary tasks. A simplified version named OT-based NCE score sacrifices accuracy to be more efficient, but it can be further improved. Consequently
settings. The recently proposed OTCE score solves this problem by considering both domain and task differences, with the help of transfer experiences on auxiliary tasks
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which causes an efficiency overhead. In this work,
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to further improve the cross-domain cross-task transferability estimation performance, which is more efficient than the OTCE score and more accurate than the OT-based NCE score
that dramatically improves the robustness of the task difference estimation in OTCE, thus removing the need for auxiliary tasks
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a ground cost
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OT-based NCE score with about
auxiliary-task free version of OTCE for
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