Unsupervised selective labeling for semi-supervised industrial defect detection

Author:

Ge JianORCID,Qin Qin,Song Shaojing,Jiang JinhuaORCID,Shen ZhiweiORCID

Publisher

Elsevier BV

Reference52 articles.

1. Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al., 2021. Extracting training data from large language models. In: 30th USENIX Security Symposium. USENIX Security 21, pp. 2633–2650.

2. Chen, B., Li, P., Chen, X., Wang, B., Zhang, L., Hua, X.-S., 2022. Dense learning based semi-supervised object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4815–4824.

3. Mixed pseudo labels for semi-supervised object detection;Chen,2023

4. Selection via proxy: Efficient data selection for deep learning;Coleman,2019

5. Revisiting consistency regularization for semi-supervised learning;Fan;Int. J. Comput. Vis.,2023

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