השוואת שיטות

	רשת קונבולוציה אדפטיבית לתחום	רשת קונבולוציה מכווננת (Fine-Tuned Convolutional Neural Network)
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2015–2017	2012–2014
הוגה השיטה	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
סוג	Domain-adaptive deep learning model	Transfer learning technique (supervised fine-tuning)
מקור מכונן	Ganin, Y., Ustinova, 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.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
כינויים	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
קשורות	5	5
תקציר	A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation.	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.