Multimodal learning: Difference between revisions

Revision as of 11:57, 6 August 2023

Revision as of 15:42, 17 December 2023

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{{machine learning}}

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'''Multimodal learning''', in the context of [[machine learning]], is a type of [[deep learning]] fromusing a combination of various [[Modality (human–computer interaction)|modalities]] of data, often arising in real-world applications. An example of multi-modal data is data that combines text (typically represented as [[feature vector]]) with imaging data consisting of [[pixel]] intensities and annotation tags. As these modalities have fundamentally different statistical properties, combining them is non-trivial, which is why specialized modelling strategies and algorithms are required. The model is then trained to able to understand and work with multiple forms of data.

==Motivation==