Multimodal learning: Difference between revisions

Revision as of 10:31, 3 April 2024

Revision as of 13:55, 1 June 2024

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{{Short description|Machine learning methods using multiple input modalities}}

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{{technical|date=June 2015}}

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{{machine learning}}

'''Multimodal learning''', in the context of [[machine learning]], is a type of [[deep learning]] using multiple [[Modality (human–computer interaction)|modalities]] of data, such as text, audio, or images.

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'''Multimodal learning''', in the context of [[machine learning]], is a type of [[deep learning]] using a combination of various [[Modality (human–computer interaction)|modalities]] of data, such as text, audio, or images, in order to create a more robust model of the real-world phenomena in question. In contrast, singular modal learning would analyze text (typically represented as [[feature vector]]) or imaging data (consisting of [[pixel]] intensities and annotation tags) independently. Multimodal machine learning combines these fundamentally different statistical analyses using specialized modeling strategies and algorithms, resulting in a model that comes closer to representing the real world.

In contrast, unimodal models can process only one type of data, such as text (typically represented as [[feature vector|feature vectors]]) or images. Multimodal learning is different from combining unimodal models trained independently. It combines information from different modalities in order to make better predictions.<ref>{{Cite web |last=Rosidi |first=Nate |date=March 27, 2023 |title=Multimodal Models Explained |url=https://www.kdnuggets.com/multimodal-models-explained |access-date=2024-06-01 |website=KDnuggets |language=en-US}}</ref>

Large multimodal models, such as [[Google Gemini]] and [[GPT-4o]], have become increasingly popular since 2023, enabling increased versatility and a more robust model of real-world phenomena.<ref>{{Cite web |last=Zia |first=Tehseen |date=January 8, 2024 |title=Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024 |url=https://www.unite.ai/unveiling-of-large-multimodal-models-shaping-the-landscape-of-language-models-in-2024/ |access-date=2024-06-01 |website=Unite.ai}}</ref>

==Motivation==

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==References==

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[[Category:Artificial neural networks]]

[[Category:Multimodal interaction]]