Porovnať metódy

	Multimodálny viacvrstvový perceptrón	Multimodálne vety vloženia
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2011 (multimodal extension); 1986 (MLP backpropagation)	2013–2021
Tvorca	Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Typ	Feedforward neural network with multi-stream fusion	Representation learning model
Pôvodný zdroj	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696.	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.
Ďalšie názvy	MM-MLP, multimodal MLP, multi-input feedforward network, fusion multilayer perceptron	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Príbuzné	5	1
Zhrnutie	A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing it through fully connected layers to produce a classification or regression output.	Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.