Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| LoRA y PEFT× | Red Generativa Antagónica× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2022 | 2014 |
| Autor original≠ | Hu, E. J. et al.; Lester, B. et al. | Goodfellow, I. et al. |
| Tipo≠ | Parameter-efficient fine-tuning of large pretrained models | Generative deep learning (adversarial two-network game) |
| Fuente seminal≠ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Alias≠ | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
| ScholarGateConjunto de datos ↗ |
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