So sánh phương pháp

	Phân loại ảnh thích ứng miền	Phân loại ảnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2015–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Người khởi xướng	Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Loại	Domain adaptation / transfer learning	Supervised classification task
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.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Tên gọi khác	domain adaptation for image classification, DAIC, cross-domain image classification, domain-shift-robust image recognition	visual classification, image recognition, CNN-based classification, visual categorization
Liên quan	3	5
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.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.