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| Βελτιστοποίηση Άμεσης Προτίμησης× | Μοντέλα Διάχυσης σε Λανθάνοντα Χώρο× | |
|---|---|---|
| Πεδίο | Βαθιά Μάθηση | Βαθιά Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2023 | 2022 |
| Δημιουργός≠ | Rafael Rafailov | Robin Rombach |
| Τύπος≠ | Training methodology | Neural network architecture |
| Θεμελιώδης πηγή≠ | Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290. link ↗ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | DPO, Direct preference | LDM, Stable Diffusion, Latent Diffusion |
| Συναφείς | 4 | 4 |
| Σύνοψη≠ | Direct Preference Optimization (DPO) is a training method introduced by Rafailov et al. in 2023 that aligns language models with human preferences without requiring an explicit reward model. By directly optimizing for preference pairs (better response vs worse response), DPO simplifies the training pipeline compared to reinforcement learning from human feedback (RLHF). | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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