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| 준지도 학습 인스턴스 분할× | 준지도 학습 객체 탐지 (Semi-supervised Object Detection)× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018–2021 | 2020–2021 |
| 창시자≠ | Multiple independent research groups (2018–2021) | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) |
| 유형≠ | Semi-supervised deep learning for dense prediction | Semi-supervised learning for detection |
| 원전≠ | Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118. link ↗ | Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757. link ↗ |
| 별칭 | Semi-supervised Mask R-CNN, pseudo-label instance segmentation, label-efficient instance segmentation, SSIS | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection |
| 관련 | 6 | 6 |
| 요약≠ | Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost. | Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines. |
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