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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Ricerca Architetturale Neurale× | Apprendimento per trasferimento× | |
|---|---|---|
| Campo≠ | Apprendimento profondo | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2017 | 2010 (formalized); 1990s (early roots) |
| Ideatore≠ | Zoph, B. & Le, Q.V. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tipo≠ | Automated architecture optimization (deep learning) | Learning paradigm |
| Fonte seminale≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Alias | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateInsieme di dati ↗ |
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