Summary of Rep: Resource-efficient Prompting For Rehearsal-free Continual Learning, by Sungho Jeon et al.
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning
by Sungho Jeon, Xinyue Ma, Kwang In Kim, Myeongjae Jeon
First submitted to arxiv on: 7 Jun 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 A recent paper introduces Resource-Efficient Prompting (REP), a method that improves the computational and memory efficiency of prompt-based rehearsal-free methods for vision-related continual learning with drifting data. This approach employs swift prompt selection, adaptive token merging (AToM), and layer dropping (ALD) to refine input data using a carefully provisioned model while minimizing accuracy trade-offs. REP is compared to state-of-the-art ViT- and CNN-based methods on multiple image classification datasets, demonstrating its superior resource efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary REP helps continual learning with drifting data by being more efficient with resources like computer power and memory. It does this by choosing the right prompts quickly and updating them in a way that skips over unimportant parts. This makes it better than other approaches for tasks like image classification. |
Keywords
» Artificial intelligence » Cnn » Continual learning » Image classification » Prompt » Prompting » Token » Vit
