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| GPTファインチューニング× | LoRAとPEFT× | ビジョントランスフォーマー× | |
|---|---|---|---|
| 分野 | 深層学習 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning | Machine learning |
| 提唱年≠ | 2019 | 2022 | 2021 |
| 提唱者≠ | Radford, A. et al. (OpenAI) | Hu, E. J. et al.; Lester, B. et al. | Dosovitskiy, A. et al. |
| 種類≠ | Fine-tuning of pretrained autoregressive language models | Parameter-efficient fine-tuning of large pretrained models | Transformer architecture for images (self-attention over patches) |
| 原典≠ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| 別名≠ | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| 関連 | 5 | 5 | 5 |
| 概要≠ | 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. | 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. | 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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