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Transferability estimation is a fundamental problem in transfer learning topredict how good the performance is when transferring a source model (or sourcetask) to a target task. With the guidance of transferability score, we canefficiently select the highly transferable source models without performing thereal transfer in practice. Recent analytical transferability metrics are mainlydesigned for image classification problem, and currently there is no specificinvestigation for the transferability estimation of semantic segmentation task,which is an essential problem in autonomous driving, medical image analysis,etc. Consequently, we further extend the recent analytical transferabilitymetric OTCE (Optimal Transport based Conditional Entropy) score to the semanticsegmentation task. The challenge in applying the OTCE score is the highdimensional segmentation output, which is difficult to find the optimalcoupling between so many pixels under an acceptable computation cost. Thus wepropose to randomly sample N pixels for computing OTCE score and take theexpectation over K repetitions as the final transferability score. Experimentalevaluation on Cityscapes, BDD100K and GTA5 datasets demonstrates that the OTCEscore highly correlates with the transfer performance.

Transferability Estimation for Semantic Segmentation Task