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From an empirical perspective, the subset chosen through active learningcannot guarantee an advantage over random sampling when transferred to anothermodel. While it underscores the significance of verifying transferability,experimental design from previous works often neglected that theinformativeness of a data subset can change over model configurations. Totackle this issue, we introduce a new experimental design, coined as CandidateProposal, to find transferable data candidates from which active learningalgorithms choose the informative subset. Correspondingly, a data selectionalgorithm is proposed, namely Transferable candidate proposal with BoundedUncertainty (TBU), which constrains the pool of transferable data candidates byfiltering out the presumably redundant data points based on uncertaintyestimation. We verified the validity of TBU in image classification benchmarks,including CIFAR-10/100 and SVHN. When transferred to different modelconfigurations, TBU consistency improves performance in existing activelearning algorithms. Our code is available athttps://github.com/gokyeongryeol/TBU.

Transferable Candidate Proposal with Bounded Uncertainty