Σύγκριση μεθόδων

	Ημι-επιβλεπόμενο GRU	Μνήμη Μακράς Βραχείας Διάρκειας (LSTM)
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014–2015	1997
Δημιουργός	Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)	Hochreiter, S. & Schmidhuber, J.
Τύπος	Semi-supervised sequence model	Recurrent neural network with gated memory cells
Θεμελιώδης πηγή	Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Εναλλακτικές ονομασίες	Semi-supervised GRU, SSL-GRU, GRU with unlabeled data, semi-supervised recurrent classifier	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Συναφείς	5	4
Σύνοψη	Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow.	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.