পদ্ধতির তুলনা করুন

	ডোমেইন-অভিযোজিত কনভোল্যুশনাল নিউরাল নেটওয়ার্ক	ডোমেইন-অ্যাডাপ্টিভ রিকারেন্ট নিউরাল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015–2017	2010s
প্রবর্তক	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
ধরন	Domain-adaptive deep learning model	Domain-adaptive sequential model
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	5	6
সারসংক্ষেপ	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.