65 citations · 221 across the 23 of their papers we have counts for
5 papers · 1 filter
Temporal Output Discrepancy for Loss Estimation-based Active Learning
Siyu Huang, Tianyang Wang, Haoyi Xiong +3
While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of dat…
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
Siyu Huang, Jie An, Donglai Wei +2
The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However,…
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang +4
Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to th…
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Leander Lauenburg, Zudi Lin, Ruihan Zhang +6
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segme…
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…