Comparer des méthodes

	Apprentissage par transfert avec segmentation d'instances	Apprentissage par transfert pour la classification d'images
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2017 (Mask R-CNN); transfer learning paradigm: 2010	2010–2012
Auteur d'origine	He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Type	Transfer learning applied to instance segmentation	Transfer learning / supervised classification
Source fondatrice	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.
Alias	pretrained instance segmentation, fine-tuned Mask R-CNN, transfer learning for panoptic segmentation, domain-adapted instance segmentation	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
Apparentées	4	4
Résumé	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 Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training from scratch.