Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Ajustarea fină a modelelor GPT× | Vision Transformer× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2019 | 2021 |
| Autorul original≠ | Radford, A. et al. (OpenAI) | Dosovitskiy, A. et al. |
| Tip≠ | Fine-tuning of pretrained autoregressive language models | Transformer architecture for images (self-attention over patches) |
| Sursa seminală≠ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Denumiri alternative | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Înrudite | 5 | 5 |
| Rezumat≠ | GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
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