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Workshop: Adaptive Experimental Design and Active Learning in the Real World

LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning

Jifan Zhang · Yifang Chen · Gregory Canal · Stephen Mussmann · Yinglun Zhu · Simon Du · Kevin Jamieson · Robert Nowak

Abstract: Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be $\text{\textit{label-efficient}}$: achieving high predictive performance from relatively few labeled examples. While obtaining the best label-efficiency in practice often requires combinations of these techniques, existing benchmark and evaluation frameworks do not capture a concerted combination of all such techniques. This paper addresses this deficiency by introducing LabelBench, a new computationally-efficient framework for joint evaluation of multiple label-efficient learning techniques. As an application of LabelBench, we introduce a novel benchmark of state-of-the-art active learning methods in combination with semi-supervised learning for fine-tuning pretrained vision transformers. Our benchmark demonstrates significantly better label-efficiencies than previously reported in active learning. LabelBench's modular codebase is open-sourced for the broader community to contribute label-efficient learning methods and benchmarks. The repository can be found at:

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