Summary of Unlocking Tuning-free Few-shot Adaptability in Visual Foundation Models by Recycling Pre-tuned Loras, By Zixuan Hu and Yongxian Wei and Li Shen and Chun Yuan and Dacheng Tao
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
by Zixuan Hu, Yongxian Wei, Li Shen, Chun Yuan, Dacheng Tao
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the potential of reusing pre-trained Low-Rank Adaptation (LoRA) modules without accessing their original training data, enabling tuning-free few-shot adaptation in Visual Foundation Models (VFMs). The authors develop a framework called LoRA Recycle that distills a meta-LoRA from diverse pre-tuned LoRAs using a meta-learning objective and surrogate data generated inversely from the pre-tuned LoRAs. This allows VFMs to solve new few-shot tasks in a single forward pass, similar to Large Language Models (LLMs) in-context learning. The framework also incorporates a double-efficient mechanism that accelerates the meta-training process while maintaining or improving performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can learn and adapt quickly without needing lots of training data. It’s like teaching a student new skills by reusing old lessons they already know! The researchers created a special framework called LoRA Recycle that takes many pre-trained learning modules and combines them to make a new, powerful module. This new module lets computers solve new problems in just one step, just like how we humans learn from experiences. The team tested their framework on different tasks and showed it works really well. |
Keywords
» Artificial intelligence » Few shot » Lora » Low rank adaptation » Meta learning
