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| Multimodales Rekurrentes Neuronales Netz× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2011–2015 | 1997 |
| Urheber≠ | 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 |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. |
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