We benchmark CCR on two large-scale information retrieval datasets, where we actively learn the most relevant documents using baseline models and crowd ...
Furthermore, thanks to the implementation of the crowdsourcing approach, we can limit the amount of work needed to annotate large sets of data. We also proposed ...
Feb 15, 2020 · Learn. (2007). MozafariB. et al. Scaling up crowd-sourcing to very large datasets: A case for active learning. PVLDB. (2014). There are more ...
this approach allows scaling up to large pools any. AL strategies and ... can easily scale to extremely large (>1B) datasets with retrieval times in ...
May 30, 2021 · ... Scaling up crowd-sourcing to very large datasets: a case for active learning. Proc VLDB Endow 8:125–136. Article Google Scholar. Nguyen AT ...
Dec 10, 2019 · Abstract:Modern machine learning algorithms need large datasets to be trained. Crowdsourcing has become a popular approach to label large ...
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a need to label large-scale and complex data. This has fos- tered the development of new active learning frameworks that make use of crowdsourced ...
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Experiments on both text and image datasets demonstrate that our proposed method outperforms other state-of-the-art active learning methods. References. [1].
Jul 13, 2023 · robust empirical results on large retrieval datasets. Despite its strengths, BM25 exhibits limitations in understanding semantic or complex ...
Kinect@ home: Crowdsourcing a large 3d dataset of real environments. In ... Tracking with Active Learning. NIPS 20. Vondrick C, Ramanan D, Patterson D ...