Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Multimodal GRU× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2014–2017 | 1997 |
| Ophavsperson≠ | Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups | Hochreiter, S. & Schmidhuber, J. |
| Type≠ | Recurrent neural network (multimodal variant) | Recurrent neural network with gated memory cells |
| Oprindelig kilde≠ | Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Aliasser | MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Relaterede≠ | 6 | 4 |
| Resumé≠ | Multimodal GRU extends the Gated Recurrent Unit architecture to jointly process sequential data from multiple input modalities — such as text, audio, and video frames — within a single recurrent framework. By fusing modality-specific encodings at the input or hidden-state level, it captures temporal dependencies across heterogeneous data streams and is widely used in multimodal sentiment analysis, video understanding, and audio-visual speech recognition. | 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. |
| ScholarGateDatasæt ↗ |
|
|