Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models
Published in TMLR, 2024
This paper is about balancing overall cost (labeling cost and training cost) and active learning sampling time. Some intriguing empirical analysis on which part of sample selection difference between the proxy model (used in efficient AL) and the fine-tuned model (used in standard AL) contribute to AL performance drops and why.
Recommended citation: Ziting Wen, Oscar Pizarro, and Stefan B. Williams. "Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models." Transactions on Machine Learning Research (2024).
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