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	डोमेन-अनुकूली कनवोल्यूशनल न्यूरल नेटवर्क	डोमेन-अनुकूली आवर्ती तंत्रिका नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2015–2017	2010s
प्रवर्तक	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
प्रकार	Domain-adaptive deep learning model	Domain-adaptive sequential model
मौलिक स्रोत	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.	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.
उपनाम	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN
संबंधित	5	6
सारांश	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.	A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable.