Salīdzināt metodes

	Adaptīvs domēna rekurentais neironu tīkls	Atmiņas ilgtermiņa īstermiņa (LSTM) arhitektūra
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	1997
Autors	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)	Hochreiter, S. & Schmidhuber, J.
Tips	Domain-adaptive sequential model	Recurrent neural network with gated memory cells
Pirmavots	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.
Citi nosaukumi	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Saistītās	6	4
Kopsavilkums	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.