Summary of Lessons Learned From a Unifying Empirical Study Of Parameter-efficient Transfer Learning (petl) in Visual Recognition, by Zheda Mai et al.
Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Transfer Learning (PETL) in Visual Recognition
by Zheda Mai, Ping Zhang, Cheng-Hao Tu, Hong-You Chen, Li Zhang, Wei-Lun Chao
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The paper presents a systematic study on parameter-efficient transfer learning (PETL) methods in the context of Vision Transformers. The authors compare representative PETL methods, tuning their hyper-parameters to fairly evaluate their accuracy on downstream tasks. The study reveals that different PETL methods can achieve similar accuracy in low-shot benchmarks like VTAB-1K if tuned carefully. Additionally, it shows that PETL methods make different mistakes and high-confidence predictions, offering opportunities for ensemble methods. The paper also explores PETL’s ability to preserve pre-trained models’ robustness to distribution shifts, finding that PETL outperforms full fine-tuning alone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PETL is a way to use large pre-trained models for smaller tasks without having to retrain everything from scratch. The authors looked at different ways to do this and compared how well they worked. They found that some methods are similar in how well they perform, but each makes different mistakes and predictions. This means we can combine them to get even better results! They also showed that PETL helps keep the pre-trained model’s ability to work well with new data. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Transfer learning
