השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| LoRA ו-PEFT× | רשת יריבות יוצרת (Generative Adversarial Network)× | מפענח אוטומטי וריאציוני× | |
|---|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 2022 | 2014 | 2014 |
| הוגה השיטה≠ | Hu, E. J. et al.; Lester, B. et al. | Goodfellow, I. et al. | Kingma, D. P. & Welling, M. |
| סוג≠ | Parameter-efficient fine-tuning of large pretrained models | Generative deep learning (adversarial two-network game) | Deep generative latent-variable model (encoder–decoder) |
| מקור מכונן≠ | 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 ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| כינויים≠ | 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 | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| קשורות≠ | 5 | 4 | 5 |
| תקציר≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
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