مقایسهٔ روش‌ها

	یادگیری انتقالی با بخش‌بندی نمونه (Transfer Learning with Instance Segmentation)	یادگیری انتقالی با تشخیص اشیاء
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2017 (Mask R-CNN); transfer learning paradigm: 2010	2010–2014
پدیدآور	He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)
نوع	Transfer learning applied to instance segmentation	Transfer learning / fine-tuning
منبع بنیادین	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
نام‌های دیگر	pretrained instance segmentation, fine-tuned Mask R-CNN, transfer learning for panoptic segmentation, domain-adapted instance segmentation	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection
مرتبط	4	3
خلاصه	Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state-of-the-art per-object mask accuracy with a fraction of the labeled data and compute that training from scratch would require.	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.