Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi Semi-Nusu-Jitoleaji wa Vitu× | Mtandao wa Mawasiliano wa Nusu-Usindikaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2020–2021 | 2013–2017 |
| Mwanzilishi≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others) |
| Aina≠ | Semi-supervised learning for detection | Semi-supervised deep learning |
| Chanzo asilia≠ | 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 ↗ | Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning. link ↗ |
| Majina mbadala | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection | SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort. |
| ScholarGateSeti ya data ↗ |
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