So sánh phương pháp

	Phân loại ảnh thích ứng miền	Transfer Learning với Phân loại Ảnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2015–2016	2010–2012
Người khởi xướng	Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)	Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)
Loại	Domain adaptation / transfer learning	Transfer learning / supervised classification
Công trình gốc	Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Tên gọi khác	domain adaptation for image classification, DAIC, cross-domain image classification, domain-shift-robust image recognition	pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC
Liên quan	3	4
Tóm tắt	Domain-adaptive image classification trains a visual classifier on a labeled source domain and adapts it to a target domain where labeled data are scarce or absent. By aligning feature distributions across domains, the model retains discriminative accuracy on the target distribution without requiring full target re-annotation, making it practical in real-world deployment scenarios where domain shift is unavoidable.	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.