Feb 20, 2024 · We con- duct experiments on various datasets, which include two types of synthetic crowd-sourcing datasets (independent and dependent cases) and three real- ...

Apr 8, 2024 · In this section, we provide the background on im- balanced learning and present the challenges of applying AL to imbalanced datasets. Then, we dis- cuss ...

Apr 8, 2024 · Using a small, fixed-sized subpool AnchorAL allows scaling any active learning strategy to large pools. By dynamically selecting different anchors at each ...

Aug 2, 2023 · Data labeling costs. Creating labeled data for large-scale training is quite expensive and time-consuming. The pricing details of Data Labelling by Google Cloud ...

Feb 2, 2024 · Large-Scale Linked Data Integration Using Probabilistic Reasoning and Crowdsourcing. In: VLDB Journal, Volume 22, Issue 5 (2013), Page 665-687, Special ...

Jan 15, 2024 · Combining human and machine intelligence in large-scale crowdsourcing. ... Scaling up crowd-sourcing to very large datasets: A case for active learning.

Mar 25, 2024 · Introduction. We introduce NetEaseCrowd, a large-scale crowdsourcing annotation dataset based on a mature Chinese data crowdsourcing platform of NetEase Inc.

Jul 5, 2023 · This makes. VeSSAL attractive even for fixed datasets that might be extremely large or fractured, as these are often interacted with using streaming, ...

Sep 5, 2023 · These include improving existing dataset, crowd sourcing and active learning based methods ... The challenge of scaling up crowdsourced labeling is significant.

Sep 30, 2023 · In the case of presence-absence data, at training time we have access to ... large-scale crowd collected species observation datasets [45; 13].