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| Multimodales Graph-Neuronales Netz× | Multimodaler Variational Autoencoder× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019–2020 | 2018 |
| Urheber≠ | Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020 | Wu, M. and Goodman, N. |
| Typ≠ | Graph-based deep learning with multimodal input fusion | Generative latent-variable model |
| Wegweisende Quelle≠ | Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). link ↗ | Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ |
| Aliasnamen | MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network | MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model |
| Verwandt≠ | 6 | 3 |
| Zusammenfassung≠ | A Multimodal Graph Neural Network (MM-GNN) combines data from multiple modalities — such as text, images, and structured features — into a unified graph structure and applies graph-based message passing to learn joint representations. It enables relational reasoning across heterogeneous data sources, going beyond what unimodal or simple concatenation approaches can capture. | The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities is observed at test time. |
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