Methoden vergelijken

	Gated Recurrent Unit (GRU)	Multimodale Transformer
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2014	2019–2021
Grondlegger	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.	Lu et al. (ViLBERT); Radford et al. (CLIP)
Type	Recurrent neural network with gating	Cross-modal attention-based deep learning model
Oorspronkelijke bron	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.	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.
Aliassen	GRU, GRU network, gated RNN, GRU cell	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Verwant	3	5
Samenvatting	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.	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.