Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Multimodální rekurentní neuronová síť× | Dlouhodobá krátkodobá paměť (LSTM)× | |
|---|---|---|
| Obor | Hluboké učení | Hluboké učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2011–2015 | 1997 |
| Tvůrce≠ | Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015) | Hochreiter, S. & Schmidhuber, J. |
| Typ≠ | Multimodal sequence model (recurrent) | Recurrent neural network with gated memory cells |
| Původní zdroj≠ | Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and Tell: A Neural Image Caption Generator. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Další názvy | MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Příbuzné≠ | 6 | 4 |
| Shrnutí≠ | A Multimodal Recurrent Neural Network combines inputs from two or more data modalities — such as images, text, and audio — within a recurrent sequence-processing framework. It encodes each modality separately, fuses the representations, and then processes the combined signal through recurrent units (RNN, LSTM, or GRU) to generate or classify sequential outputs. This design made it a foundational approach in image captioning, video description, 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. |
| ScholarGateDatová sada ↗ |
|
|