Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| LSTM× | Transformer Multimodal× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1997 | 2019–2021 |
| Autorul original≠ | Hochreiter, S. & Schmidhuber, J. | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| Tip≠ | Recurrent neural network (gated memory cell) | Cross-modal attention-based deep learning model |
| Sursa seminală≠ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ |
| Denumiri alternative | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| Înrudite | 5 | 5 |
| Rezumat≠ | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. | A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis. |
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