Dec 10, 2021 · To address this challenge, we propose two schemes, namely expected-gradnorm and entropy-gradnorm. The former computes the gradient norm by ...
The goal of AL is to obtain the best test performance of the task model given a specific annotation budget.
In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss ...
Dec 10, 2021 · Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or ...
May 2, 2022 · Bibliographic details on Boosting Active Learning via Improving Test Performance.
Jan 23, 2022 · In this paper, we theoretically analyze the connection between data selection and the test performance of the task model used in active learning ...
Boosting Active Learning via Improving Test Performance · 1 code implementation • 10 Dec 2021 • Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu ...
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Oct 24, 2024 · Active Learning (AL) is a pivotal approach in enhancing test performance through efficient data annotation and model training.
Aug 1, 2024 · Active learning (AL) aims to reduce the annotation labor required for deep learning by selecting the most informative samples from among ...
Missing: Improving Test
Through extensive empirical experiments, we bring the performance of active learning methods to a new level: an absolute performance boost of 16.99% on CIFAR-10 ...