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\section{Conclusion}
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We have demonstrated existing state-of-the-art methods can be suboptimal in single round selection. We show that under certain budget for pretraining, \algname shall achieve better generalization performance compared to other active learning algorithms, and that most of validation accuracy improvement is realized by our two-stage algorithm. Finally, we illustrate how behaviors of all algorithms change with variation of pretraining and single round acquisition budget across multiple datasets and architectures.
One potential direction for future work could be to determine an optimal budget allocation for both the pretraining and acquisition stages, as well as the extension of \algname to the few-rounds setting. %learning generalizable acquisition functions for few-round setting of AL with limited adaptivity.