手法を比較
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| 半教師あり物体検出× | 物体検出における転移学習× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2020–2021 | 2010–2014 |
| 提唱者≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework) |
| 種類≠ | Semi-supervised learning for detection | Transfer learning / fine-tuning |
| 原典≠ | 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 ↗ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 別名 | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection | pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection |
| 関連≠ | 6 | 3 |
| 概要≠ | 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. | Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require. |
| ScholarGateデータセット ↗ |
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