قارن الطرق

	شبكة عصبية متكررة متكيفة مع المجال	الذاكرة طويلة قصيرة المدى (LSTM)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2010s	1997
صاحب الطريقة	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)	Hochreiter, S. & Schmidhuber, J.
النوع	Domain-adaptive sequential model	Recurrent neural network with gated memory cells
المصدر التأسيسي	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.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
الأسماء البديلة	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
ذات صلة	6	4
الملخص	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.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.