Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Semi-supervised Instance Segmentation× | Mtandao wa Mawasiliano wa Nusu-Usindikaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2018–2021 | 2013–2017 |
| Mwanzilishi≠ | Multiple independent research groups (2018–2021) | Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others) |
| Aina≠ | Semi-supervised deep learning for dense prediction | Semi-supervised deep learning |
| Chanzo asilia≠ | 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 ↗ | 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 | Semi-supervised Mask R-CNN, pseudo-label instance segmentation, label-efficient instance segmentation, SSIS | SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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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