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| Μοντέλα Διάχυσης σε Λανθάνοντα Χώρο× | GraphRAG× | |
|---|---|---|
| Πεδίο | Βαθιά Μάθηση | Βαθιά Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2022 | 2023 |
| Δημιουργός≠ | Robin Rombach | Yunfan Gao |
| Τύπος≠ | Neural network architecture | System architecture |
| Θεμελιώδης πηγή≠ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI ↗ | Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, M. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997. link ↗ |
| Εναλλακτικές ονομασίες≠ | LDM, Stable Diffusion, Latent Diffusion | Graph RAG, Knowledge Graph RAG |
| Συναφείς | 4 | 4 |
| Σύνοψη≠ | Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality. | GraphRAG is a retrieval-augmented generation approach that augments large language models with knowledge graphs to improve answer quality and factuality. Rather than retrieving flat text passages, GraphRAG constructs and queries structured knowledge graphs extracted from documents, providing rich contextual information to the language model. |
| ScholarGateΣύνολο δεδομένων ↗ |
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