Due to the black-box nature of machine learning models, the latent space may be completely unintuitive.
Such techniques include t-distributed stochastic neighbor embedding (t-SNE), where the latent space is mapped to two dimensions for visualization.
Multimodal embedding models aim to learn joint representations that fuse information from multiple modalities, allowing for cross-modal analysis and tasks.
These models enable applications like image captioning, visual question answering, and multimodal sentiment analysis.
The resulting embeddings capture the complex relationships between different data types, facilitating multimodal analysis and understanding.