Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Трансферное обучение с использованием LSTM× | Сверточный рекуррентный блок (GRU)× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2018 (ULMFiT; concept since ~2010) | 2014 |
| Автор метода≠ | Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010) | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. |
| Тип≠ | Transfer learning / Sequential model | Recurrent neural network with gating |
| Основополагающий источник≠ | Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339. DOI ↗ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734. link ↗ |
| Другие названия | LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer | GRU, GRU network, gated RNN, GRU cell |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce. | The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM. |
| ScholarGateНабор данных ↗ |
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